[102] Abhijit Bendale and Terrance E Boult. Towards open set deep networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1563-1572, 2016.
[103] Haoran Wang, Weitang Liu, Alex Bocchieri, and Yixuan Li. Can multi-label classification networks know what they don't know? Proceedings of the Advances in Neural Information Processing Systems, 2021.
[104] Qitian Wu, Yiting Chen, Chenxiao Yang, and Junchi Yan. Energy-based out-of-distribution detection for graph neural networks. In International Conference on Learning Representations, 2023.
[105] Jihoon Tack, Sangwoo Mo, Jongheon Jeong, and Jinwoo Shin. Csi: Novelty detection via contrastive learning on distributionally shifted instances. In Advances in Neural Information Processing Systems, 2020.
[106] Vikash Sehwag, Mung Chiang, and Prateek Mittal. Ssd: A unified framework for self-supervised outlier detection. In International Conference on Learning Representations, 2021.
[107] Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mohammad Saleh, Balaji Lakshminarayanan, and Peter J Liu. Out-of-distribution detection and selective generation for conditional language models. In International Conference on Learning Representations, 2023.
[108] Rheeya Uppaal, Junjie Hu, and Yixuan Li. Is fine-tuning needed? pre-trained language models are near perfect for out-of-domain detection. In Annual Meeting of the Association for Computational Linguistics, 2023.
[109] 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, 2021.
[110] Yarin Gal and Zoubin Ghahramani. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In Proceedings of the International Conference on Machine Learning, pages 1050–1059, 2016.
[111] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. In Advances in Neural Information Processing Systems, volume 30, pages 6402-6413, 2017.
[112] Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson. A simple baseline for bayesian uncertainty in deep learning. Advances in Neural Information Processing Systems, 32:13153-13164, 2019.
[113] Andrey Malinin and Mark Gales. Reverse kl-divergence training of prior networks: Improved uncertainty and adversarial robustness. In Advances in Neural Information Processing Systems, 2019.
[114] Yeming Wen, Dustin Tran, and Jimmy Ba. Batchensemble: an alternative approach to efficient ensemble and lifelong learning. In International Conference on Learning Representations, 2020.
[115] Agustinus Kristiadi, Matthias Hein, and Philipp Hennig. Being bayesian, even just a bit, fixes overconfidence in relu networks. In International conference on machine learning, pages 5436-5446, 2020.
[116] Petra Bevandić, Ivan Krešo, Marin Oršić, and Sinisa Šegvic. Discriminative out-of-distribution detection for semantic segmentation. arXiv preprint arXiv:1808.07703, 2018.
[117] Andrey Malinin and Mark Gales. Predictive uncertainty estimation via prior networks. Advances in Neural Information Processing Systems, 31, 2018.
[118] Yonatan Geifman and Ran El-Yaniv. Selectivenet: A deep neural network with an integrated reject option. In Proceedings of the International Conference on Machine Learning, pages 2151-2159, 2019.