References#
Bibliographic references to scio‘s implemented Scores.
Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joseph Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Song. Scaling Out-of-Distribution detection for real-world settings. In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, 8759–8773. 2022. URL: https://proceedings.mlr.press/v162/hendrycks22a.html.
Dan Hendrycks and Kevin Gimpel. A baseline for detecting misclassified and Out-of-Distribution examples in Neural Networks. In International Conference on Learning Representations. 2017. URL: https://openreview.net/forum?id=Hkg4TI9xl.
Rui Huang, Andrew Geng, and Yixuan Li. On the importance of gradients for detecting distributional shifts in the wild. In Advances in Neural Information Processing Systems, volume 34, 677–689. 2021. URL: https://proceedings.nips.cc/paper_files/paper/2021/file/063e26c670d07bb7c4d30e6fc69fe056-Paper.pdf.
Heinrich Jiang, Been Kim, Melody Guan, and Maya Gupta. To trust or not to trust a classifier. In Advances in Neural Information Processing Systems, volume 31. 2018. URL: https://proceedings.nips.cc/paper_files/paper/2018/file/7180cffd6a8e829dacfc2a31b3f72ece-Paper.pdf.
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. A simple unified framework for detecting Out-of-Distribution samples and adversarial attacks. In Advances in Neural Information Processing Systems, volume 31. 2018. URL: https://proceedings.nips.cc/paper_files/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf.
Shiyu Liang, Yixuan Li, and Rayadurgam Srikant. Enhancing the reliability of Out-of-Distribution image detection in Neural Networks. In International Conference on Learning Representations. 2018. URL: https://openreview.net/forum?id=H1VGkIxRZ.
Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li. Energy-based Out-of-Distribution detection. In Advances in Neural Information Processing Systems, volume 33, 21464–21475. 2020. URL: https://proceedings.nips.cc/paper_files/paper/2020/file/f5496252609c43eb8a3d147ab9b9c006-Paper.pdf.
Xingjun Ma, Bo Li, Yisen Wang, Sarah M. Erfani, Sudanthi Wijewickrema, Grant Schoenebeck, Michael E. Houle, Dawn Song, and James Bailey. Characterizing adversarial subspaces using Local Intrinsic Dimensionality. In International Conference on Learning Representations. 2018. URL: https://openreview.net/forum?id=B1gJ1L2aW.
David Macêdo, Tsang Ing Ren, Cleber Zanchettin, Adriano L. I. Oliveira, and Teresa Ludermir. Entropic Out-of-Distribution detection: seamless detection of unknown examples. IEEE Transactions on Neural Networks and Learning Systems, 33(6):2350–2364, 2022. doi:10.1109/TNNLS.2021.3112897.
Nicolas Papernot and Patrick McDaniel. Deep k-nearest neighbors: towards confident, interpretable and robust Deep Learning. arXiv, 2018. URL: https://arxiv.org/abs/1803.04765.
Jayaram Raghuram, Varun Chandrasekaran, Somesh Jha, and Suman Banerjee. A general framework for detecting anomalous inputs to DNN classifiers. In Proceedings of the 38th International Conference on Machine Learning, volume 139, 8764–8775. 2021. URL: https://proceedings.mlr.press/v139/raghuram21a.html.
Jie Ren, Stanislav Fort, Jeremiah Zhe Liu, Abhijit Guha Roy, Shreyas Padhy, and Balaji Lakshminarayanan. A simple fix to Mahalanobis distance for improving near-OoD detection. In ICML workshop: Uncertainty & Robustness in Deep Learning. Poster. 2021. URL: http://www.gatsby.ucl.ac.uk/~balaji/udl2021/accepted-papers/UDL2021-paper-007.pdf.
Kevin Roth, Yannic Kilcher, and Thomas Hofmann. The Odds are Odd: a statistical test for detecting adversarial examples. In Proceedings of the 36th International Conference on Machine Learning, volume 97, 5498–5507. 2019. URL: https://proceedings.mlr.press/v97/roth19a.html.
Chandramouli Shama Sastry and Sageev Oore. Detecting Out-of-Distribution examples with Gram matrices. In International Conference on Machine Learning, volume 119, 8491–8501. 2020. URL: http://proceedings.mlr.press/v119/sastry20a/sastry20a.pdf.
Yiyou Sun, Chuan Guo, and Yixuan Li. ReAct: Out-of-Distribution detection with rectified activations. In Advances in Neural Information Processing Systems, volume 34, 144–157. 2021. URL: https://proceedings.nips.cc/paper_files/paper/2021/file/01894d6f048493d2cacde3c579c315a3-Paper.pdf.
Yiyou Sun, Yifei Ming, Xiaojin Zhu, and Yixuan Li. Out-of-Distribution detection with deep nearest neighbors. In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, 20827–20840. 2022. URL: https://proceedings.mlr.press/v162/sun22d.html.
Haoran Wang, Weitang Liu, Alex Bocchieri, and Yixuan Li. Can multi-label classification networks know what they don’t know? In Advances in Neural Information Processing Systems, volume 34, 29074–29087. 2021. URL: https://proceedings.nips.cc/paper_files/paper/2021/file/f3b7e5d3eb074cde5b76e26bc0fb5776-Paper.pdf.
Weilin Xu, David Evans, and Yanjun Qi. Feature Squeezing: detecting adversarial examples in Deep Neural Networks. In Proceedings 2018 Network and Distributed System Security Symposium. 2018. URL: https://arxiv.org/pdf/1704.01155, doi:10.14722/ndss.2018.23198.