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Uncertainty estimates for semantic segmentation: providing enhanced reliability for automated motor claims handling (2401.09245v2)

Published 17 Jan 2024 in cs.CV

Abstract: Deep neural network models for image segmentation can be a powerful tool for the automation of motor claims handling processes in the insurance industry. A crucial aspect is the reliability of the model outputs when facing adverse conditions, such as low quality photos taken by claimants to document damages. We explore the use of a meta-classification model to empirically assess the precision of segments predicted by a model trained for the semantic segmentation of car body parts. Different sets of features correlated with the quality of a segment are compared, and an AUROC score of 0.915 is achieved for distinguishing between high- and low-quality segments. By removing low-quality segments, the average mIoU of the segmentation output is improved by 16 percentage points and the number of wrongly predicted segments is reduced by 77%.

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References (17)
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[2019] Hein, M., Andriushchenko, M., Bitterwolf, J.: Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, pp. 41–50 (2019). https://doi.org/10.1109/CVPR.2019.00013 Guo et al. [2017] Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: Proceedings of the 34th International Conference on Machine Learning - Volume 70. ICML’17, pp. 1321–1330. JMLR.org, Sydney, NSW, Australia (2017) Kendall et al. [2017] Kendall, A., Badrinarayananm, V., Cipolla, R.: Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding. In: Kim, T.-K., Zafeiriou, S., Brostow, G., Mikolajczyk, K. (eds.) Proceedings of the British Machine Vision Conference (BMVC), pp. 1–12. BMVA Press, London, United Kingdom (2017). https://doi.org/10.5244/C.31.57 Lee et al. [2020] Lee, H.J., Kim, S.T., Lee, H., Navab, N., Ro, Y.M.: Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation (2020) Holder and Shafique [2021] Holder, C.J., Shafique, M.: Efficient uncertainty estimation in semantic segmentation via distillation. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 3080–3087 (2021). https://doi.org/10.1109/ICCVW54120.2021.00343 Lin and Hauptmann [2003] Lin, W.-H., Hauptmann, A.: Meta-classification: Combining multimodal classifiers. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds.) Mining Multimedia and Complex Data, pp. 217–231. Springer, Berlin, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39666-6_14 Rottmann et al. [2020] Rottmann, M., Colling, P., Paul Hack, T., Chan, R., Huger, F., Schlicht, P., Gottschalk, H.: Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities. 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ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Yu, Y., Wang, C., Fu, Q., Kou, R., Huang, F., Yang, B., Yang, T., Gao, M.: Techniques and challenges of image segmentation: A review. Electronics 12(5) (2023) https://doi.org/10.3390/electronics12051199 Mehrtash et al. [2020] Mehrtash, A., Wells, W.M., Tempany, C.M., Abolmaesumi, P., Kapur, T.: Confidence calibration and predictive uncertainty estimation for deep medical image segmentation. IEEE Transactions on Medical Imaging 39(12), 3868–3878 (2020) https://doi.org/10.1109/tmi.2020.3006437 1911.13273 Hein et al. [2019] Hein, M., Andriushchenko, M., Bitterwolf, J.: Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, pp. 41–50 (2019). https://doi.org/10.1109/CVPR.2019.00013 Guo et al. [2017] Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: Proceedings of the 34th International Conference on Machine Learning - Volume 70. ICML’17, pp. 1321–1330. JMLR.org, Sydney, NSW, Australia (2017) Kendall et al. [2017] Kendall, A., Badrinarayananm, V., Cipolla, R.: Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding. In: Kim, T.-K., Zafeiriou, S., Brostow, G., Mikolajczyk, K. (eds.) Proceedings of the British Machine Vision Conference (BMVC), pp. 1–12. BMVA Press, London, United Kingdom (2017). https://doi.org/10.5244/C.31.57 Lee et al. [2020] Lee, H.J., Kim, S.T., Lee, H., Navab, N., Ro, Y.M.: Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation (2020) Holder and Shafique [2021] Holder, C.J., Shafique, M.: Efficient uncertainty estimation in semantic segmentation via distillation. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 3080–3087 (2021). https://doi.org/10.1109/ICCVW54120.2021.00343 Lin and Hauptmann [2003] Lin, W.-H., Hauptmann, A.: Meta-classification: Combining multimodal classifiers. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds.) Mining Multimedia and Complex Data, pp. 217–231. Springer, Berlin, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39666-6_14 Rottmann et al. [2020] Rottmann, M., Colling, P., Paul Hack, T., Chan, R., Huger, F., Schlicht, P., Gottschalk, H.: Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, Glasgow, United Kingdom (2020). https://doi.org/10.1109/IJCNN48605.2020.9206659 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: 5th International Conference on Learning Representations, ICLR 2017. OpenReview.net, Toulon, France (2017). https://openreview.net/forum?id=Hkg4TI9xl Maag and Riedlinger [2023] Maag, K., Riedlinger, T.: Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation (2023). https://doi.org/10.48550/arXiv.2303.06920 Jaccard [1912] Jaccard, P.: The distribution of the flora in the alpine zone. The New Phytologist 11(2), 37–50 (1912) https://doi.org/10.1111/j.1469-8137.1912.tb05611.x Chen and Guestrin [2016] Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Mehrtash, A., Wells, W.M., Tempany, C.M., Abolmaesumi, P., Kapur, T.: Confidence calibration and predictive uncertainty estimation for deep medical image segmentation. IEEE Transactions on Medical Imaging 39(12), 3868–3878 (2020) https://doi.org/10.1109/tmi.2020.3006437 1911.13273 Hein et al. [2019] Hein, M., Andriushchenko, M., Bitterwolf, J.: Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, pp. 41–50 (2019). https://doi.org/10.1109/CVPR.2019.00013 Guo et al. [2017] Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: Proceedings of the 34th International Conference on Machine Learning - Volume 70. ICML’17, pp. 1321–1330. JMLR.org, Sydney, NSW, Australia (2017) Kendall et al. [2017] Kendall, A., Badrinarayananm, V., Cipolla, R.: Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding. In: Kim, T.-K., Zafeiriou, S., Brostow, G., Mikolajczyk, K. (eds.) Proceedings of the British Machine Vision Conference (BMVC), pp. 1–12. BMVA Press, London, United Kingdom (2017). https://doi.org/10.5244/C.31.57 Lee et al. [2020] Lee, H.J., Kim, S.T., Lee, H., Navab, N., Ro, Y.M.: Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation (2020) Holder and Shafique [2021] Holder, C.J., Shafique, M.: Efficient uncertainty estimation in semantic segmentation via distillation. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 3080–3087 (2021). https://doi.org/10.1109/ICCVW54120.2021.00343 Lin and Hauptmann [2003] Lin, W.-H., Hauptmann, A.: Meta-classification: Combining multimodal classifiers. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds.) Mining Multimedia and Complex Data, pp. 217–231. Springer, Berlin, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39666-6_14 Rottmann et al. [2020] Rottmann, M., Colling, P., Paul Hack, T., Chan, R., Huger, F., Schlicht, P., Gottschalk, H.: Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, Glasgow, United Kingdom (2020). https://doi.org/10.1109/IJCNN48605.2020.9206659 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: 5th International Conference on Learning Representations, ICLR 2017. OpenReview.net, Toulon, France (2017). https://openreview.net/forum?id=Hkg4TI9xl Maag and Riedlinger [2023] Maag, K., Riedlinger, T.: Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation (2023). https://doi.org/10.48550/arXiv.2303.06920 Jaccard [1912] Jaccard, P.: The distribution of the flora in the alpine zone. The New Phytologist 11(2), 37–50 (1912) https://doi.org/10.1111/j.1469-8137.1912.tb05611.x Chen and Guestrin [2016] Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Hein, M., Andriushchenko, M., Bitterwolf, J.: Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, pp. 41–50 (2019). https://doi.org/10.1109/CVPR.2019.00013 Guo et al. [2017] Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: Proceedings of the 34th International Conference on Machine Learning - Volume 70. ICML’17, pp. 1321–1330. JMLR.org, Sydney, NSW, Australia (2017) Kendall et al. [2017] Kendall, A., Badrinarayananm, V., Cipolla, R.: Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding. In: Kim, T.-K., Zafeiriou, S., Brostow, G., Mikolajczyk, K. (eds.) Proceedings of the British Machine Vision Conference (BMVC), pp. 1–12. BMVA Press, London, United Kingdom (2017). https://doi.org/10.5244/C.31.57 Lee et al. [2020] Lee, H.J., Kim, S.T., Lee, H., Navab, N., Ro, Y.M.: Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation (2020) Holder and Shafique [2021] Holder, C.J., Shafique, M.: Efficient uncertainty estimation in semantic segmentation via distillation. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 3080–3087 (2021). https://doi.org/10.1109/ICCVW54120.2021.00343 Lin and Hauptmann [2003] Lin, W.-H., Hauptmann, A.: Meta-classification: Combining multimodal classifiers. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds.) Mining Multimedia and Complex Data, pp. 217–231. Springer, Berlin, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39666-6_14 Rottmann et al. [2020] Rottmann, M., Colling, P., Paul Hack, T., Chan, R., Huger, F., Schlicht, P., Gottschalk, H.: Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, Glasgow, United Kingdom (2020). https://doi.org/10.1109/IJCNN48605.2020.9206659 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: 5th International Conference on Learning Representations, ICLR 2017. OpenReview.net, Toulon, France (2017). https://openreview.net/forum?id=Hkg4TI9xl Maag and Riedlinger [2023] Maag, K., Riedlinger, T.: Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation (2023). https://doi.org/10.48550/arXiv.2303.06920 Jaccard [1912] Jaccard, P.: The distribution of the flora in the alpine zone. The New Phytologist 11(2), 37–50 (1912) https://doi.org/10.1111/j.1469-8137.1912.tb05611.x Chen and Guestrin [2016] Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: Proceedings of the 34th International Conference on Machine Learning - Volume 70. ICML’17, pp. 1321–1330. JMLR.org, Sydney, NSW, Australia (2017) Kendall et al. [2017] Kendall, A., Badrinarayananm, V., Cipolla, R.: Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding. In: Kim, T.-K., Zafeiriou, S., Brostow, G., Mikolajczyk, K. (eds.) Proceedings of the British Machine Vision Conference (BMVC), pp. 1–12. BMVA Press, London, United Kingdom (2017). https://doi.org/10.5244/C.31.57 Lee et al. [2020] Lee, H.J., Kim, S.T., Lee, H., Navab, N., Ro, Y.M.: Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation (2020) Holder and Shafique [2021] Holder, C.J., Shafique, M.: Efficient uncertainty estimation in semantic segmentation via distillation. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 3080–3087 (2021). https://doi.org/10.1109/ICCVW54120.2021.00343 Lin and Hauptmann [2003] Lin, W.-H., Hauptmann, A.: Meta-classification: Combining multimodal classifiers. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds.) Mining Multimedia and Complex Data, pp. 217–231. Springer, Berlin, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39666-6_14 Rottmann et al. [2020] Rottmann, M., Colling, P., Paul Hack, T., Chan, R., Huger, F., Schlicht, P., Gottschalk, H.: Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, Glasgow, United Kingdom (2020). https://doi.org/10.1109/IJCNN48605.2020.9206659 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: 5th International Conference on Learning Representations, ICLR 2017. OpenReview.net, Toulon, France (2017). https://openreview.net/forum?id=Hkg4TI9xl Maag and Riedlinger [2023] Maag, K., Riedlinger, T.: Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation (2023). https://doi.org/10.48550/arXiv.2303.06920 Jaccard [1912] Jaccard, P.: The distribution of the flora in the alpine zone. The New Phytologist 11(2), 37–50 (1912) https://doi.org/10.1111/j.1469-8137.1912.tb05611.x Chen and Guestrin [2016] Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Kendall, A., Badrinarayananm, V., Cipolla, R.: Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding. In: Kim, T.-K., Zafeiriou, S., Brostow, G., Mikolajczyk, K. (eds.) Proceedings of the British Machine Vision Conference (BMVC), pp. 1–12. BMVA Press, London, United Kingdom (2017). https://doi.org/10.5244/C.31.57 Lee et al. [2020] Lee, H.J., Kim, S.T., Lee, H., Navab, N., Ro, Y.M.: Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation (2020) Holder and Shafique [2021] Holder, C.J., Shafique, M.: Efficient uncertainty estimation in semantic segmentation via distillation. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 3080–3087 (2021). https://doi.org/10.1109/ICCVW54120.2021.00343 Lin and Hauptmann [2003] Lin, W.-H., Hauptmann, A.: Meta-classification: Combining multimodal classifiers. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds.) Mining Multimedia and Complex Data, pp. 217–231. Springer, Berlin, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39666-6_14 Rottmann et al. [2020] Rottmann, M., Colling, P., Paul Hack, T., Chan, R., Huger, F., Schlicht, P., Gottschalk, H.: Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, Glasgow, United Kingdom (2020). https://doi.org/10.1109/IJCNN48605.2020.9206659 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: 5th International Conference on Learning Representations, ICLR 2017. OpenReview.net, Toulon, France (2017). https://openreview.net/forum?id=Hkg4TI9xl Maag and Riedlinger [2023] Maag, K., Riedlinger, T.: Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation (2023). https://doi.org/10.48550/arXiv.2303.06920 Jaccard [1912] Jaccard, P.: The distribution of the flora in the alpine zone. The New Phytologist 11(2), 37–50 (1912) https://doi.org/10.1111/j.1469-8137.1912.tb05611.x Chen and Guestrin [2016] Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Lee, H.J., Kim, S.T., Lee, H., Navab, N., Ro, Y.M.: Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation (2020) Holder and Shafique [2021] Holder, C.J., Shafique, M.: Efficient uncertainty estimation in semantic segmentation via distillation. 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[2020] Rottmann, M., Colling, P., Paul Hack, T., Chan, R., Huger, F., Schlicht, P., Gottschalk, H.: Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, Glasgow, United Kingdom (2020). https://doi.org/10.1109/IJCNN48605.2020.9206659 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: 5th International Conference on Learning Representations, ICLR 2017. OpenReview.net, Toulon, France (2017). https://openreview.net/forum?id=Hkg4TI9xl Maag and Riedlinger [2023] Maag, K., Riedlinger, T.: Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation (2023). https://doi.org/10.48550/arXiv.2303.06920 Jaccard [1912] Jaccard, P.: The distribution of the flora in the alpine zone. 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In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Rottmann, M., Colling, P., Paul Hack, T., Chan, R., Huger, F., Schlicht, P., Gottschalk, H.: Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, Glasgow, United Kingdom (2020). https://doi.org/10.1109/IJCNN48605.2020.9206659 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: 5th International Conference on Learning Representations, ICLR 2017. OpenReview.net, Toulon, France (2017). https://openreview.net/forum?id=Hkg4TI9xl Maag and Riedlinger [2023] Maag, K., Riedlinger, T.: Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation (2023). https://doi.org/10.48550/arXiv.2303.06920 Jaccard [1912] Jaccard, P.: The distribution of the flora in the alpine zone. The New Phytologist 11(2), 37–50 (1912) https://doi.org/10.1111/j.1469-8137.1912.tb05611.x Chen and Guestrin [2016] Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. 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In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Jaccard, P.: The distribution of the flora in the alpine zone. The New Phytologist 11(2), 37–50 (1912) https://doi.org/10.1111/j.1469-8137.1912.tb05611.x Chen and Guestrin [2016] Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. 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  7. Lee, H.J., Kim, S.T., Lee, H., Navab, N., Ro, Y.M.: Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation (2020) Holder and Shafique [2021] Holder, C.J., Shafique, M.: Efficient uncertainty estimation in semantic segmentation via distillation. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 3080–3087 (2021). https://doi.org/10.1109/ICCVW54120.2021.00343 Lin and Hauptmann [2003] Lin, W.-H., Hauptmann, A.: Meta-classification: Combining multimodal classifiers. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds.) Mining Multimedia and Complex Data, pp. 217–231. Springer, Berlin, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39666-6_14 Rottmann et al. [2020] Rottmann, M., Colling, P., Paul Hack, T., Chan, R., Huger, F., Schlicht, P., Gottschalk, H.: Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities. 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Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Holder, C.J., Shafique, M.: Efficient uncertainty estimation in semantic segmentation via distillation. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 3080–3087 (2021). https://doi.org/10.1109/ICCVW54120.2021.00343 Lin and Hauptmann [2003] Lin, W.-H., Hauptmann, A.: Meta-classification: Combining multimodal classifiers. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds.) Mining Multimedia and Complex Data, pp. 217–231. Springer, Berlin, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39666-6_14 Rottmann et al. [2020] Rottmann, M., Colling, P., Paul Hack, T., Chan, R., Huger, F., Schlicht, P., Gottschalk, H.: Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, Glasgow, United Kingdom (2020). https://doi.org/10.1109/IJCNN48605.2020.9206659 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: 5th International Conference on Learning Representations, ICLR 2017. OpenReview.net, Toulon, France (2017). https://openreview.net/forum?id=Hkg4TI9xl Maag and Riedlinger [2023] Maag, K., Riedlinger, T.: Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation (2023). https://doi.org/10.48550/arXiv.2303.06920 Jaccard [1912] Jaccard, P.: The distribution of the flora in the alpine zone. The New Phytologist 11(2), 37–50 (1912) https://doi.org/10.1111/j.1469-8137.1912.tb05611.x Chen and Guestrin [2016] Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Lin, W.-H., Hauptmann, A.: Meta-classification: Combining multimodal classifiers. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds.) Mining Multimedia and Complex Data, pp. 217–231. Springer, Berlin, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39666-6_14 Rottmann et al. [2020] Rottmann, M., Colling, P., Paul Hack, T., Chan, R., Huger, F., Schlicht, P., Gottschalk, H.: Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, Glasgow, United Kingdom (2020). https://doi.org/10.1109/IJCNN48605.2020.9206659 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: 5th International Conference on Learning Representations, ICLR 2017. OpenReview.net, Toulon, France (2017). https://openreview.net/forum?id=Hkg4TI9xl Maag and Riedlinger [2023] Maag, K., Riedlinger, T.: Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation (2023). https://doi.org/10.48550/arXiv.2303.06920 Jaccard [1912] Jaccard, P.: The distribution of the flora in the alpine zone. The New Phytologist 11(2), 37–50 (1912) https://doi.org/10.1111/j.1469-8137.1912.tb05611.x Chen and Guestrin [2016] Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Rottmann, M., Colling, P., Paul Hack, T., Chan, R., Huger, F., Schlicht, P., Gottschalk, H.: Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, Glasgow, United Kingdom (2020). https://doi.org/10.1109/IJCNN48605.2020.9206659 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: 5th International Conference on Learning Representations, ICLR 2017. OpenReview.net, Toulon, France (2017). https://openreview.net/forum?id=Hkg4TI9xl Maag and Riedlinger [2023] Maag, K., Riedlinger, T.: Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation (2023). https://doi.org/10.48550/arXiv.2303.06920 Jaccard [1912] Jaccard, P.: The distribution of the flora in the alpine zone. The New Phytologist 11(2), 37–50 (1912) https://doi.org/10.1111/j.1469-8137.1912.tb05611.x Chen and Guestrin [2016] Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: 5th International Conference on Learning Representations, ICLR 2017. OpenReview.net, Toulon, France (2017). https://openreview.net/forum?id=Hkg4TI9xl Maag and Riedlinger [2023] Maag, K., Riedlinger, T.: Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation (2023). https://doi.org/10.48550/arXiv.2303.06920 Jaccard [1912] Jaccard, P.: The distribution of the flora in the alpine zone. The New Phytologist 11(2), 37–50 (1912) https://doi.org/10.1111/j.1469-8137.1912.tb05611.x Chen and Guestrin [2016] Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Maag, K., Riedlinger, T.: Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation (2023). https://doi.org/10.48550/arXiv.2303.06920 Jaccard [1912] Jaccard, P.: The distribution of the flora in the alpine zone. The New Phytologist 11(2), 37–50 (1912) https://doi.org/10.1111/j.1469-8137.1912.tb05611.x Chen and Guestrin [2016] Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Jaccard, P.: The distribution of the flora in the alpine zone. The New Phytologist 11(2), 37–50 (1912) https://doi.org/10.1111/j.1469-8137.1912.tb05611.x Chen and Guestrin [2016] Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. 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  8. Holder, C.J., Shafique, M.: Efficient uncertainty estimation in semantic segmentation via distillation. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 3080–3087 (2021). https://doi.org/10.1109/ICCVW54120.2021.00343 Lin and Hauptmann [2003] Lin, W.-H., Hauptmann, A.: Meta-classification: Combining multimodal classifiers. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds.) Mining Multimedia and Complex Data, pp. 217–231. Springer, Berlin, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39666-6_14 Rottmann et al. [2020] Rottmann, M., Colling, P., Paul Hack, T., Chan, R., Huger, F., Schlicht, P., Gottschalk, H.: Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, Glasgow, United Kingdom (2020). https://doi.org/10.1109/IJCNN48605.2020.9206659 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: 5th International Conference on Learning Representations, ICLR 2017. OpenReview.net, Toulon, France (2017). https://openreview.net/forum?id=Hkg4TI9xl Maag and Riedlinger [2023] Maag, K., Riedlinger, T.: Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation (2023). https://doi.org/10.48550/arXiv.2303.06920 Jaccard [1912] Jaccard, P.: The distribution of the flora in the alpine zone. The New Phytologist 11(2), 37–50 (1912) https://doi.org/10.1111/j.1469-8137.1912.tb05611.x Chen and Guestrin [2016] Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Lin, W.-H., Hauptmann, A.: Meta-classification: Combining multimodal classifiers. In: Zaïane, O.R., Simoff, S.J., Djeraba, C. (eds.) Mining Multimedia and Complex Data, pp. 217–231. Springer, Berlin, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39666-6_14 Rottmann et al. [2020] Rottmann, M., Colling, P., Paul Hack, T., Chan, R., Huger, F., Schlicht, P., Gottschalk, H.: Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, Glasgow, United Kingdom (2020). https://doi.org/10.1109/IJCNN48605.2020.9206659 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: 5th International Conference on Learning Representations, ICLR 2017. OpenReview.net, Toulon, France (2017). https://openreview.net/forum?id=Hkg4TI9xl Maag and Riedlinger [2023] Maag, K., Riedlinger, T.: Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation (2023). https://doi.org/10.48550/arXiv.2303.06920 Jaccard [1912] Jaccard, P.: The distribution of the flora in the alpine zone. The New Phytologist 11(2), 37–50 (1912) https://doi.org/10.1111/j.1469-8137.1912.tb05611.x Chen and Guestrin [2016] Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Rottmann, M., Colling, P., Paul Hack, T., Chan, R., Huger, F., Schlicht, P., Gottschalk, H.: Prediction error meta classification in semantic segmentation: Detection via aggregated dispersion measures of softmax probabilities. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, Glasgow, United Kingdom (2020). https://doi.org/10.1109/IJCNN48605.2020.9206659 Hendrycks and Gimpel [2017] Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: 5th International Conference on Learning Representations, ICLR 2017. OpenReview.net, Toulon, France (2017). https://openreview.net/forum?id=Hkg4TI9xl Maag and Riedlinger [2023] Maag, K., Riedlinger, T.: Pixel-wise Gradient Uncertainty for Convolutional Neural Networks applied to Out-of-Distribution Segmentation (2023). https://doi.org/10.48550/arXiv.2303.06920 Jaccard [1912] Jaccard, P.: The distribution of the flora in the alpine zone. The New Phytologist 11(2), 37–50 (1912) https://doi.org/10.1111/j.1469-8137.1912.tb05611.x Chen and Guestrin [2016] Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 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  14. Chen, T., Guestrin, C.: XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785 Shwartz-Ziv and Armon [2022] Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874
  15. Shwartz-Ziv, R., Armon, A.: Tabular data: Deep learning is not all you need. Information Fusion 81, 84–90 (2022) https://doi.org/10.1016/j.inffus.2021.11.011 Pedregosa et al. [2011] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874
  16. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011) 1201.0490 Davis and Goadrich [2006] Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874 Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874
  17. Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning. ICML ’06, pp. 233–240. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143844.1143874
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