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RandoMix: A mixed sample data augmentation method with multiple mixed modes (2205.08728v2)
Published 18 May 2022 in cs.CV
Abstract: Data augmentation plays a crucial role in enhancing the robustness and performance of machine learning models across various domains. In this study, we introduce a novel mixed-sample data augmentation method called RandoMix. RandoMix is specifically designed to simultaneously address robustness and diversity challenges. It leverages a combination of linear and mask-mixed modes, introducing flexibility in candidate selection and weight adjustments. We evaluate the effectiveness of RandoMix on diverse datasets, including CIFAR-10/100, Tiny-ImageNet, ImageNet, and Google Speech Commands. Our results demonstrate its superior performance compared to existing techniques such as Mixup, CutMix, Fmix, and ResizeMix. Notably, RandoMix excels in enhancing model robustness against adversarial noise, natural noise, and sample occlusion. The comprehensive experimental results and insights into parameter tuning underscore the potential of RandoMix as a versatile and effective data augmentation method. Moreover, it seamlessly integrates into the training pipeline.
- Kamath, U., Liu, J., Whitaker, J.: Deep Learning for NLP and Speech Recognition vol. 84. Springer (3) Vapnik, V.: On the uniform convergence of relative frequencies of events to their probabilities. In: Doklady Akademii Nauk USSR, vol. 181, pp. 781–787 (1968) (4) Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. International Conference on Learning Representations (ICLR) (2018) (5) Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: Regularization strategy to train strong classifiers with localizable features. IEEE International Conference on Computer Vision (ICCV) (2019) (6) Qin, J., Fang, J., Zhang, Q., Liu, W., Wang, X., Wang, X.: Resizemix: Mixing data with preserved object information and true labels. arXiv preprint arXiv:2012.11101 (2020) (7) Harris, E., Marcu, A., Painter, M., Niranjan, M., Hare, A.P.-B.J.: Fmix: Enhancing mixed sample data augmentation. International Conference on Learning Representations (ICLR) (2021) (8) Kim, J.-H., Choo, W., Song, H.O.: Puzzle mix: Exploiting saliency and local statistics for optimal mixup. International Conference on Machine Learning (ICML) (2020) (9) Kim, J., Choo, W., Jeong, H., Song, H.O.: Co-mixup: Saliency guided joint mixup with supermodular diversity. In: International Conference on Learning Representations (ICLR) (2021) (10) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Vapnik, V.: On the uniform convergence of relative frequencies of events to their probabilities. In: Doklady Akademii Nauk USSR, vol. 181, pp. 781–787 (1968) (4) Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. International Conference on Learning Representations (ICLR) (2018) (5) Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: Regularization strategy to train strong classifiers with localizable features. IEEE International Conference on Computer Vision (ICCV) (2019) (6) Qin, J., Fang, J., Zhang, Q., Liu, W., Wang, X., Wang, X.: Resizemix: Mixing data with preserved object information and true labels. arXiv preprint arXiv:2012.11101 (2020) (7) Harris, E., Marcu, A., Painter, M., Niranjan, M., Hare, A.P.-B.J.: Fmix: Enhancing mixed sample data augmentation. International Conference on Learning Representations (ICLR) (2021) (8) Kim, J.-H., Choo, W., Song, H.O.: Puzzle mix: Exploiting saliency and local statistics for optimal mixup. International Conference on Machine Learning (ICML) (2020) (9) Kim, J., Choo, W., Jeong, H., Song, H.O.: Co-mixup: Saliency guided joint mixup with supermodular diversity. In: International Conference on Learning Representations (ICLR) (2021) (10) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. International Conference on Learning Representations (ICLR) (2018) (5) Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: Regularization strategy to train strong classifiers with localizable features. IEEE International Conference on Computer Vision (ICCV) (2019) (6) Qin, J., Fang, J., Zhang, Q., Liu, W., Wang, X., Wang, X.: Resizemix: Mixing data with preserved object information and true labels. arXiv preprint arXiv:2012.11101 (2020) (7) Harris, E., Marcu, A., Painter, M., Niranjan, M., Hare, A.P.-B.J.: Fmix: Enhancing mixed sample data augmentation. International Conference on Learning Representations (ICLR) (2021) (8) Kim, J.-H., Choo, W., Song, H.O.: Puzzle mix: Exploiting saliency and local statistics for optimal mixup. International Conference on Machine Learning (ICML) (2020) (9) Kim, J., Choo, W., Jeong, H., Song, H.O.: Co-mixup: Saliency guided joint mixup with supermodular diversity. In: International Conference on Learning Representations (ICLR) (2021) (10) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: Regularization strategy to train strong classifiers with localizable features. IEEE International Conference on Computer Vision (ICCV) (2019) (6) Qin, J., Fang, J., Zhang, Q., Liu, W., Wang, X., Wang, X.: Resizemix: Mixing data with preserved object information and true labels. arXiv preprint arXiv:2012.11101 (2020) (7) Harris, E., Marcu, A., Painter, M., Niranjan, M., Hare, A.P.-B.J.: Fmix: Enhancing mixed sample data augmentation. International Conference on Learning Representations (ICLR) (2021) (8) Kim, J.-H., Choo, W., Song, H.O.: Puzzle mix: Exploiting saliency and local statistics for optimal mixup. International Conference on Machine Learning (ICML) (2020) (9) Kim, J., Choo, W., Jeong, H., Song, H.O.: Co-mixup: Saliency guided joint mixup with supermodular diversity. In: International Conference on Learning Representations (ICLR) (2021) (10) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Qin, J., Fang, J., Zhang, Q., Liu, W., Wang, X., Wang, X.: Resizemix: Mixing data with preserved object information and true labels. arXiv preprint arXiv:2012.11101 (2020) (7) Harris, E., Marcu, A., Painter, M., Niranjan, M., Hare, A.P.-B.J.: Fmix: Enhancing mixed sample data augmentation. International Conference on Learning Representations (ICLR) (2021) (8) Kim, J.-H., Choo, W., Song, H.O.: Puzzle mix: Exploiting saliency and local statistics for optimal mixup. International Conference on Machine Learning (ICML) (2020) (9) Kim, J., Choo, W., Jeong, H., Song, H.O.: Co-mixup: Saliency guided joint mixup with supermodular diversity. In: International Conference on Learning Representations (ICLR) (2021) (10) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Harris, E., Marcu, A., Painter, M., Niranjan, M., Hare, A.P.-B.J.: Fmix: Enhancing mixed sample data augmentation. International Conference on Learning Representations (ICLR) (2021) (8) Kim, J.-H., Choo, W., Song, H.O.: Puzzle mix: Exploiting saliency and local statistics for optimal mixup. International Conference on Machine Learning (ICML) (2020) (9) Kim, J., Choo, W., Jeong, H., Song, H.O.: Co-mixup: Saliency guided joint mixup with supermodular diversity. In: International Conference on Learning Representations (ICLR) (2021) (10) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Kim, J.-H., Choo, W., Song, H.O.: Puzzle mix: Exploiting saliency and local statistics for optimal mixup. International Conference on Machine Learning (ICML) (2020) (9) Kim, J., Choo, W., Jeong, H., Song, H.O.: Co-mixup: Saliency guided joint mixup with supermodular diversity. In: International Conference on Learning Representations (ICLR) (2021) (10) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Kim, J., Choo, W., Jeong, H., Song, H.O.: Co-mixup: Saliency guided joint mixup with supermodular diversity. In: International Conference on Learning Representations (ICLR) (2021) (10) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. 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Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: Regularization strategy to train strong classifiers with localizable features. IEEE International Conference on Computer Vision (ICCV) (2019) (6) Qin, J., Fang, J., Zhang, Q., Liu, W., Wang, X., Wang, X.: Resizemix: Mixing data with preserved object information and true labels. arXiv preprint arXiv:2012.11101 (2020) (7) Harris, E., Marcu, A., Painter, M., Niranjan, M., Hare, A.P.-B.J.: Fmix: Enhancing mixed sample data augmentation. International Conference on Learning Representations (ICLR) (2021) (8) Kim, J.-H., Choo, W., Song, H.O.: Puzzle mix: Exploiting saliency and local statistics for optimal mixup. International Conference on Machine Learning (ICML) (2020) (9) Kim, J., Choo, W., Jeong, H., Song, H.O.: Co-mixup: Saliency guided joint mixup with supermodular diversity. In: International Conference on Learning Representations (ICLR) (2021) (10) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Qin, J., Fang, J., Zhang, Q., Liu, W., Wang, X., Wang, X.: Resizemix: Mixing data with preserved object information and true labels. arXiv preprint arXiv:2012.11101 (2020) (7) Harris, E., Marcu, A., Painter, M., Niranjan, M., Hare, A.P.-B.J.: Fmix: Enhancing mixed sample data augmentation. International Conference on Learning Representations (ICLR) (2021) (8) Kim, J.-H., Choo, W., Song, H.O.: Puzzle mix: Exploiting saliency and local statistics for optimal mixup. International Conference on Machine Learning (ICML) (2020) (9) Kim, J., Choo, W., Jeong, H., Song, H.O.: Co-mixup: Saliency guided joint mixup with supermodular diversity. In: International Conference on Learning Representations (ICLR) (2021) (10) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Harris, E., Marcu, A., Painter, M., Niranjan, M., Hare, A.P.-B.J.: Fmix: Enhancing mixed sample data augmentation. International Conference on Learning Representations (ICLR) (2021) (8) Kim, J.-H., Choo, W., Song, H.O.: Puzzle mix: Exploiting saliency and local statistics for optimal mixup. International Conference on Machine Learning (ICML) (2020) (9) Kim, J., Choo, W., Jeong, H., Song, H.O.: Co-mixup: Saliency guided joint mixup with supermodular diversity. In: International Conference on Learning Representations (ICLR) (2021) (10) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Kim, J.-H., Choo, W., Song, H.O.: Puzzle mix: Exploiting saliency and local statistics for optimal mixup. International Conference on Machine Learning (ICML) (2020) (9) Kim, J., Choo, W., Jeong, H., Song, H.O.: Co-mixup: Saliency guided joint mixup with supermodular diversity. In: International Conference on Learning Representations (ICLR) (2021) (10) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Kim, J., Choo, W., Jeong, H., Song, H.O.: Co-mixup: Saliency guided joint mixup with supermodular diversity. In: International Conference on Learning Representations (ICLR) (2021) (10) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. 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International Conference on Learning Representations (ICLR) (2019) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. 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In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. 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International Conference on Learning Representations (ICLR) (2019) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. 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In: International Conference on Learning Representations (ICLR) (2021) (10) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. 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In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Qin, J., Fang, J., Zhang, Q., Liu, W., Wang, X., Wang, X.: Resizemix: Mixing data with preserved object information and true labels. arXiv preprint arXiv:2012.11101 (2020) (7) Harris, E., Marcu, A., Painter, M., Niranjan, M., Hare, A.P.-B.J.: Fmix: Enhancing mixed sample data augmentation. International Conference on Learning Representations (ICLR) (2021) (8) Kim, J.-H., Choo, W., Song, H.O.: Puzzle mix: Exploiting saliency and local statistics for optimal mixup. International Conference on Machine Learning (ICML) (2020) (9) Kim, J., Choo, W., Jeong, H., Song, H.O.: Co-mixup: Saliency guided joint mixup with supermodular diversity. In: International Conference on Learning Representations (ICLR) (2021) (10) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. 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International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Harris, E., Marcu, A., Painter, M., Niranjan, M., Hare, A.P.-B.J.: Fmix: Enhancing mixed sample data augmentation. International Conference on Learning Representations (ICLR) (2021) (8) Kim, J.-H., Choo, W., Song, H.O.: Puzzle mix: Exploiting saliency and local statistics for optimal mixup. International Conference on Machine Learning (ICML) (2020) (9) Kim, J., Choo, W., Jeong, H., Song, H.O.: Co-mixup: Saliency guided joint mixup with supermodular diversity. In: International Conference on Learning Representations (ICLR) (2021) (10) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. 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International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. 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International Conference on Learning Representations (ICLR) (2019) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. 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International Conference on Learning Representations (ICLR) (2019) Uddin, A.F.M.S., Monira, M.S., Shin, W., Chung, T., Bae, S.-H.: Saliencymix: A saliency guided data augmentation strategy for better regularization. International Conference on Learning Representations (ICLR) (2021) (11) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. 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International Conference on Learning Representations (ICLR) (2019) Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009) (12) Chrabaszcz, P., Loshchilov, I., Hutter, F.: A downsampled variant of imagenet as an alternative to the cifar datasets. arXiv preprint arXiv:1707.08819 (2017) (13) Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. 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Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. 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International Conference on Learning Representations (ICLR) (2019) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). 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In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. 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- Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal on Computer Vision (IJCV) (2015) (14) Warden, P.: Speech commands: A public dataset for single-word speech recognition. Dataset available from http://download.tensorflow. org/data/speech_commands_v0.01.tar.gz (2017) (15) Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., Bengio, Y.: Manifold mixup: Better representations by interpolating hidden states. International Conference on Machine Learning (ICML) (2019) (16) Faramarzi, M., Amini, M., Badrinaaraayanan, A., Verma, V., Chandar, S.: Patchup: A feature-space block-level regularization technique for convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. 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In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 589–597 (2022) (17) DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017) (18) He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: European Conference on Computer Vision (ECCV) (2016) (19) Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. 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In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. 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- Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Procedings of the British Machine Vision Conference 2016 (2016). British Machine Vision Association (20) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019)
- He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016) (21) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019)
- Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. In: International Conference on Learning Representations (ICLR) (2017) (22) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019)
- Brain, G.: Tensorflow speech recognition challenge. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge (2017) (23) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019)
- Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR) (2015) (24) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019) Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019)
- Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR) (2019)
- Xiaoliang Liu (8 papers)
- Furao Shen (44 papers)
- Jian Zhao (218 papers)
- Changhai Nie (6 papers)