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We study the problem of Out-of-Distribution (OOD) detection, that is, detecting whether a learning algorithm's output can be trusted at inference time.
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L. Zhang, M. Goldstein, and R. Ranganath, “Understanding failures in out-of-distribution detection with deep generative models,” in International Conference on Machine Learning . PMLR, 2021, pp. 12 427–12 436
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G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 4700–4708
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K. Lee, K. Lee, H. Lee, and J. Shin, “A simple unified framework for detecting out-of-distribution samples and adversarial attacks,” in Advances in Neural Information Processing Systems , S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Eds., vol. 31. Curran Associates, Inc., 2018. [Online]. Available: https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf
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S. Liang, Y. Li, and R. Srikant, “Enhancing the reliability of out-of-distribution image detection in neural networks,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=H1VGkIxRZ
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D. Hendrycks, M. Mazeika, and T. G. Dietterich, “Deep anomaly detection with outlier exposure,” in 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 . OpenReview.net, 2019. [Online]. Available: https://openreview.net/forum?id=HyxCxhRcY7
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C. S. Sastry and S. Oore, “Detecting out-of-distribution examples with gram matrices,” in Proceedings of the 37th International Conference on Machine Learning , ser. ICML’20. JMLR.org, 2020
2020
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W. Liu, X. Wang, J. Owens, and Y. Li, “Energy-based out-of-distribution detection,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 21 464–21 475. [Online]. Available: https://proceedings.neurips.cc/paper/2020/file/f5496252609c43eb8a3d147ab9b9c006-Paper.pdf
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2021
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2021
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2022
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2022
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F. Bergamin, P.-A. Mattei, J. D. Havtorn, H. Senetaire, H. Schmutz, L. Maaløe, S. Hauberg, and J. Frellsen, “Model-agnostic out-of-distribution detection using combined statistical tests,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2022, pp. 10 753–10 776
2022
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