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Distillation-based fabric anomaly detection (2401.02287v1)
Published 4 Jan 2024 in cs.CV
Abstract: Unsupervised texture anomaly detection has been a concerning topic in a vast amount of industrial processes. Patterned textures inspection, particularly in the context of fabric defect detection, is indeed a widely encountered use case. This task involves handling a diverse spectrum of colors and textile types, encompassing a wide range of fabrics. Given the extensive variability in colors, textures, and defect types, fabric defect detection poses a complex and challenging problem in the field of patterned textures inspection. In this article, we propose a knowledge distillation-based approach tailored specifically for addressing the challenge of unsupervised anomaly detection in textures resembling fabrics. Our method aims to redefine the recently introduced reverse distillation approach, which advocates for an encoder-decoder design to mitigate classifier bias and to prevent the student from reconstructing anomalies. In this study, we present a new reverse distillation technique for the specific task of fabric defect detection. Our approach involves a meticulous design selection that strategically highlights high-level features. To demonstrate the capabilities of our approach both in terms of performance and inference speed, we conducted a series of experiments on multiple texture datasets, including MVTEC AD, AITEX, and TILDA, alongside conducting experiments on a dataset acquired from a textile manufacturing facility. The main contributions of this paper are the following: a robust texture anomaly detector utilizing a reverse knowledge-distillation technique suitable for both anomaly detection and domain generalization and a novel dataset encompassing a diverse range of fabrics and defects.
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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks 60(6), 84–90. 10.1145/3065386. URL https://dl.acm.org/doi/10.1145/3065386 [3] M. Tan, Q.V. Le. EfficientNet: Rethinking model scaling for convolutional neural networks. URL http://arxiv.org/abs/1905.11946 [4] J. Yu, Y. Zheng, X. Wang, W. Li, Y. Wu, R. Zhao, L. Wu, FastFlow: Unsupervised anomaly detection and localization via 2d normalizing flows URL http://arxiv.org/abs/2111.07677. 2111.07677 [5] M. Rudolph, T. Wehrbein, B. Rosenhahn, B. Wandt, Fully convolutional cross-scale-flows for image-based defect detection URL http://arxiv.org/abs/2110.02855. 2110.02855 [6] K. Roth, L. Pemula, J. Zepeda, B. Schölkopf, T. Brox, P. Gehler, Towards total recall in industrial anomaly detection URL http://arxiv.org/abs/2106.08265. 2106.08265 [7] J. Bae, J.H. Lee, S. Kim. PNI : Industrial anomaly detection using position and neighborhood information. URL http://arxiv.org/abs/2211.12634 [8] M. Salehi, N. Sadjadi, S. Baselizadeh, M.H. Rohban, H.R. Rabiee, pp. 14902–14912. URL https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html [9] G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Tan, Q.V. Le. EfficientNet: Rethinking model scaling for convolutional neural networks. URL http://arxiv.org/abs/1905.11946 [4] J. Yu, Y. Zheng, X. Wang, W. Li, Y. Wu, R. Zhao, L. Wu, FastFlow: Unsupervised anomaly detection and localization via 2d normalizing flows URL http://arxiv.org/abs/2111.07677. 2111.07677 [5] M. Rudolph, T. Wehrbein, B. Rosenhahn, B. Wandt, Fully convolutional cross-scale-flows for image-based defect detection URL http://arxiv.org/abs/2110.02855. 2110.02855 [6] K. Roth, L. Pemula, J. Zepeda, B. Schölkopf, T. Brox, P. Gehler, Towards total recall in industrial anomaly detection URL http://arxiv.org/abs/2106.08265. 2106.08265 [7] J. Bae, J.H. Lee, S. Kim. PNI : Industrial anomaly detection using position and neighborhood information. URL http://arxiv.org/abs/2211.12634 [8] M. Salehi, N. Sadjadi, S. Baselizadeh, M.H. Rohban, H.R. Rabiee, pp. 14902–14912. URL https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html [9] G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 J. Yu, Y. Zheng, X. Wang, W. Li, Y. Wu, R. Zhao, L. Wu, FastFlow: Unsupervised anomaly detection and localization via 2d normalizing flows URL http://arxiv.org/abs/2111.07677. 2111.07677 [5] M. Rudolph, T. Wehrbein, B. Rosenhahn, B. Wandt, Fully convolutional cross-scale-flows for image-based defect detection URL http://arxiv.org/abs/2110.02855. 2110.02855 [6] K. Roth, L. Pemula, J. Zepeda, B. Schölkopf, T. Brox, P. Gehler, Towards total recall in industrial anomaly detection URL http://arxiv.org/abs/2106.08265. 2106.08265 [7] J. Bae, J.H. Lee, S. Kim. PNI : Industrial anomaly detection using position and neighborhood information. URL http://arxiv.org/abs/2211.12634 [8] M. Salehi, N. Sadjadi, S. Baselizadeh, M.H. Rohban, H.R. Rabiee, pp. 14902–14912. URL https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html [9] G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Rudolph, T. Wehrbein, B. Rosenhahn, B. Wandt, Fully convolutional cross-scale-flows for image-based defect detection URL http://arxiv.org/abs/2110.02855. 2110.02855 [6] K. Roth, L. Pemula, J. Zepeda, B. Schölkopf, T. Brox, P. Gehler, Towards total recall in industrial anomaly detection URL http://arxiv.org/abs/2106.08265. 2106.08265 [7] J. Bae, J.H. Lee, S. Kim. PNI : Industrial anomaly detection using position and neighborhood information. URL http://arxiv.org/abs/2211.12634 [8] M. Salehi, N. Sadjadi, S. Baselizadeh, M.H. Rohban, H.R. Rabiee, pp. 14902–14912. URL https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html [9] G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 K. Roth, L. Pemula, J. Zepeda, B. Schölkopf, T. Brox, P. Gehler, Towards total recall in industrial anomaly detection URL http://arxiv.org/abs/2106.08265. 2106.08265 [7] J. Bae, J.H. Lee, S. Kim. PNI : Industrial anomaly detection using position and neighborhood information. URL http://arxiv.org/abs/2211.12634 [8] M. Salehi, N. Sadjadi, S. Baselizadeh, M.H. Rohban, H.R. Rabiee, pp. 14902–14912. URL https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html [9] G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. 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URL https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html [9] G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. 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URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. 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Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. 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URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. 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Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. 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Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. 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Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. 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Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. 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CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. 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Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. 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Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Rudolph, T. Wehrbein, B. Rosenhahn, B. Wandt, Fully convolutional cross-scale-flows for image-based defect detection URL http://arxiv.org/abs/2110.02855. 2110.02855 [6] K. Roth, L. Pemula, J. Zepeda, B. Schölkopf, T. Brox, P. Gehler, Towards total recall in industrial anomaly detection URL http://arxiv.org/abs/2106.08265. 2106.08265 [7] J. Bae, J.H. Lee, S. Kim. PNI : Industrial anomaly detection using position and neighborhood information. URL http://arxiv.org/abs/2211.12634 [8] M. Salehi, N. Sadjadi, S. Baselizadeh, M.H. Rohban, H.R. Rabiee, pp. 14902–14912. URL https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html [9] G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. 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Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. 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URL https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html [9] G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. 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Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Salehi, N. Sadjadi, S. Baselizadeh, M.H. Rohban, H.R. Rabiee, pp. 14902–14912. URL https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html [9] G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. 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URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. 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Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. 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Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. 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Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. 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Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 H. Xie, Y. Zhang, Z. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. 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Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. 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URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Rudolph, T. Wehrbein, B. Rosenhahn, B. Wandt, Fully convolutional cross-scale-flows for image-based defect detection URL http://arxiv.org/abs/2110.02855. 2110.02855 [6] K. Roth, L. Pemula, J. Zepeda, B. Schölkopf, T. Brox, P. Gehler, Towards total recall in industrial anomaly detection URL http://arxiv.org/abs/2106.08265. 2106.08265 [7] J. Bae, J.H. Lee, S. Kim. PNI : Industrial anomaly detection using position and neighborhood information. URL http://arxiv.org/abs/2211.12634 [8] M. Salehi, N. Sadjadi, S. Baselizadeh, M.H. Rohban, H.R. Rabiee, pp. 14902–14912. URL https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html [9] G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 K. Roth, L. Pemula, J. Zepeda, B. Schölkopf, T. Brox, P. Gehler, Towards total recall in industrial anomaly detection URL http://arxiv.org/abs/2106.08265. 2106.08265 [7] J. Bae, J.H. Lee, S. Kim. PNI : Industrial anomaly detection using position and neighborhood information. URL http://arxiv.org/abs/2211.12634 [8] M. Salehi, N. Sadjadi, S. Baselizadeh, M.H. Rohban, H.R. Rabiee, pp. 14902–14912. URL https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html [9] G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. 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Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. 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URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. 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Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. 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URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. 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URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. 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Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. 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Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. 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Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. 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Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. 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Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. 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Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 J. Bae, J.H. Lee, S. Kim. PNI : Industrial anomaly detection using position and neighborhood information. URL http://arxiv.org/abs/2211.12634 [8] M. Salehi, N. Sadjadi, S. Baselizadeh, M.H. Rohban, H.R. Rabiee, pp. 14902–14912. URL https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html [9] G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. 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Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Salehi, N. Sadjadi, S. Baselizadeh, M.H. Rohban, H.R. Rabiee, pp. 14902–14912. URL https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html [9] G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. 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URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. 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Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. 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Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 H. Xie, Y. Zhang, Z. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. 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Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. 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URL https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html [9] G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. 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Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Salehi, N. Sadjadi, S. Baselizadeh, M.H. Rohban, H.R. Rabiee, pp. 14902–14912. URL https://openaccess.thecvf.com/content/CVPR2021/html/Salehi_Multiresolution_Knowledge_Distillation_for_Anomaly_Detection_CVPR_2021_paper.html [9] G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. 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Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Ojala, M. Pietikainen, T. 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URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. 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Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. 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Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. 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URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. 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Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. 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Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. 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Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. 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Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. 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Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. 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Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. 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Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. 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Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. 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Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. 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Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. 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Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Mäenpää, M. 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Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. 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Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. 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Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. 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Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 G. Wang, S. Han, E. Ding, D. Huang, Student-teacher feature pyramid matching for anomaly detection URL http://arxiv.org/abs/2103.04257. 2103.04257 [10] H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 H. Deng, X. Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. 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Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. 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URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. 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Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. 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Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. 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Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. 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Li, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9737–9746 [11] S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Lee, S. Lee, B.C. Song. CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. URL http://arxiv.org/abs/2206.04325 [12] S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Thomine, H. Snoussi, M. Soua, pp. 487–494. URL https://www.scitepress.org/Link.aspx?doi=10.5220/0011633100003417 [13] D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. 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URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. 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Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. 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Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. 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Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. 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Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. 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Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. 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Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 J. Jing, Z. Wang, M. Rätsch, H. 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Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. 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Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. 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Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. 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Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 H. Xie, Y. Zhang, Z. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. 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Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 D.J. Rezende, S. Mohamed. Variational inference with normalizing flows. URL http://arxiv.org/abs/1505.05770 [14] G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. 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V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 G. Hinton, O. Vinyals, J. Dean. Distilling the knowledge in a neural network. URL http://arxiv.org/abs/1503.02531 [15] N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 N.C. Ristea, N. Madan, R.T. Ionescu, K. Nasrollahi, F.S. Khan, T.B. Moeslund, M. Shah, Self-supervised predictive convolutional attentive block for anomaly detection URL http://arxiv.org/abs/2111.09099. 2111.09099 [16] A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. 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Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. 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Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. 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Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. 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Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. 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Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. 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Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. 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Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. 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Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. 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URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304
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URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Humeau-Heurtier, Texture feature extraction methods: A survey 7, 8975–9000. 10.1109/ACCESS.2018.2890743. Conference Name: IEEE Access [17] M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Partio, B. Cramariuc, M. Gabbouj, A. Visa, Rock texture retrieval using gray level co-occurrence matrix [18] T. Ojala, M. Pietikainen, T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. 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Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns 24(7), 971–987. 10.1109/TPAMI.2002.1017623. URL http://ieeexplore.ieee.org/document/1017623/ [19] T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. 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Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. 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Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. 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Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Mäenpää, M. Pietikäinen, TEXTURE ANALYSIS WITH LOCAL BINARY PATTERNS (WORLD SCIENTIFIC), 3rd edn., pp. 197–216. 10.1142/9789812775320_0011. URL http://www.worldscientific.com/doi/abs/10.1142/9789812775320_0011 [20] P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 P. Simon, U. V, Deep learning based feature extraction for texture classification 171, 1680–1687. 10.1016/j.procs.2020.04.180. URL https://linkinghub.elsevier.com/retrieve/pii/S1877050920311613 [21] M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. 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Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. 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Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. 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Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M.A. Kramer, Nonlinear principal component analysis using autoassociative neural networks 37(2) [22] S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Han, X. Hu, H. Huang, M. Jiang, Y. Zhao. ADBench: Anomaly detection benchmark. URL http://arxiv.org/abs/2206.09426 [23] J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. 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Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. 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Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. 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Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. 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URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. 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Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. 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Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. 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Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. 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Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. 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DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. 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Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. 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Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. 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URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. 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Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 J. Jing, Z. Wang, M. Rätsch, H. Zhang, Mobile-unet: An efficient convolutional neural network for fabric defect detection 92, 004051752092860. 10.1177/0040517520928604 [24] H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 H. Xie, Y. Zhang, Z. Wu, An improved fabric defect detection method based on SSD 8(1), 181–190. 10.14504/ajr.8.S1.22. URL http://journals.sagepub.com/doi/10.14504/ajr.8.S1.22 [25] S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Mei, Y. Wang, G. Wen, Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model 18(4), 1064. 10.3390/s18041064. URL https://www.mdpi.com/1424-8220/18/4/1064 [26] Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. 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Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. 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Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. 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Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. 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Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. 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Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. 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Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. 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Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. 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Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. 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Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Q.P. Nguyen, K.W. Lim, D.M. Divakaran, K.H. Low, M.C. Chan, GEE: A gradient-based explainable variational autoencoder for network anomaly detection URL http://arxiv.org/abs/1903.06661. 1903.06661 [27] V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 V. Zavrtanik, M. Kristan, D. Skočaj. DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. 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Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. 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Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. 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Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304
- DRAEM – a discriminatively trained reconstruction embedding for surface anomaly detection. URL http://arxiv.org/abs/2108.07610 [28] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial networks URL http://arxiv.org/abs/1406.2661. 1406.2661 [29] T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 T. Schlegl, P. Seeböck, S.M. Waldstein, G. Langs, U. Schmidt-Erfurth, f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks 54, 30–44. 10.1016/j.media.2019.01.010. URL https://linkinghub.elsevier.com/retrieve/pii/S1361841518302640 [30] M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 M. Pourreza, B. Mohammadi, M. Khaki, S. Bouindour, H. Snoussi, M. Sabokrou, in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) (IEEE), pp. 2002–2011. 10.1109/WACV48630.2021.00205. URL https://ieeexplore.ieee.org/document/9423181/. Event-place: Waikoloa, HI, USA [31] Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 Y. Liang, J. Zhang, S. Zhao, R. Wu, Y. Liu, S. Pan, Omni-frequency channel-selection representations for unsupervised anomaly detection URL http://arxiv.org/abs/2203.00259. 2203.00259 [32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is all you need. URL http://arxiv.org/abs/1706.03762 [33] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. 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Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. Li, Triplet-graph reasoning network for few-shot metal generic surface defect segmentation 70, 1–11. 10.1109/TIM.2021.3083561. Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. URL http://arxiv.org/abs/2007.09316 [48] S.F. Chen, Y.M. Liu, C.C. Lin, T.P.C. Chen, Y.C.F. Wang. Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304 J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. 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Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. Weakly supervised learning for industrial optical inspection [40] P. Mishra, R. Verk, D. Fornasier, C. Piciarelli, G.L. Foresti, in 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE). pp. 01–06. 10.1109/ISIE45552.2021.9576231. URL http://arxiv.org/abs/2104.10036 [41] Y. Bao, K. Song, J. Liu, Y. Wang, Y. Yan, H. Yu, X. 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Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale URL http://arxiv.org/abs/2010.11929. 2010.11929 [34] S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. URL http://arxiv.org/abs/1502.03167 [35] S. Thomine, H. Snoussi, M. Soua, pp. 487–494 [36] P. Bergmann, M. Fauser, D. Sattlegger, C. Steger, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE), pp. 9584–9592. 10.1109/CVPR.2019.00982. URL https://ieeexplore.ieee.org/document/8954181/ [37] G. Deutsche Forschungsgemeinschaft. TILDA textile texture-database. URL https://lmb.informatik.uni-freiburg.de/resources/datasets/tilda.en.html [38] J. Silvestre-Blanes, T. Albero-Albero, I. Miralles, R. Pérez-Llorens, J. Moreno, A public fabric database for defect detection methods and results 19(4), 363–374. doi:10.2478/aut-2019-0035. URL https://doi.org/10.2478/aut-2019-0035 [39] F.A.H. Matthias Wieler, Tobias Hahn. 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Conference Name: IEEE Transactions on Instrumentation and Measurement [42] D. Tabernik, S. Šela, J. Skvarč, D. Skočaj, Segmentation-based deep-learning approach for surface-defect detection 10.1007/s10845-019-01476-x [43] J. Lu, B. Liang, Q. Lei, X. Li, J. Liu, J. Liu, J. Xu, W. Wang, SCueU-net: Efficient damage detection method for railway rail 8, 125109–125120. 10.1109/ACCESS.2020.3007603. Conference Name: IEEE Access [44] D.P. Kingma, J. Ba. Adam: A method for stochastic optimization. URL http://arxiv.org/abs/1412.6980 [45] S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, U. Genc. Anomalib: A deep learning library for anomaly detection. URL http://arxiv.org/abs/2202.08341 [46] D. Li, J. Zhang, Y. Yang, C. Liu, Y.Z. Song, T. Hospedales, in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE), pp. 1446–1455. 10.1109/ICCV.2019.00153. URL https://ieeexplore.ieee.org/document/9008109/ [47] S. Wang, L. Yu, C. Li, C.W. Fu, P.A. Heng. 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- Domain-generalized textured surface anomaly detection. URL http://arxiv.org/abs/2203.12304