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Open-world classification systems should discern out-of-distribution (OOD) data whose labels deviate from those of in-distribution (ID) cases, motivating recent studies in OOD detection.
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Aman Sinha, Hongseok Namkoong, and John C. Duchi · 2018
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Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and R. Srikant · 2018
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Roman Vershynin · 2018
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Noah Golowich, Alexander Rakhlin, and Ohad Shamir · 2018
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Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Out-of-distribution detection using union of 1-dimensional subspaces
Alireza Zaeemzadeh, Niccolò Bisagno, Zeno Sambugaro, Nicola Conci, Nazanin Rahnavard, and Mubarak Shah · 2021
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Understanding failures in out-of-distribution detection with deep generative models
Lily H. Zhang, Mark Goldstein, and Rajesh Ranganath · 2021
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Learning bounds for open-set learning
Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, and Guangquan Zhang · 2021
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Investigating bi-level optimization for learning and vision from a unified perspective: A survey and beyond
Risheng Liu, Jiaxin Gao, Jin Zhang, Deyu Meng, and Zhouchen Lin · 2021
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ImageNet-21k pretraining for the masses
Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, and Lihi Zelnik-Manor · 2021
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Tianshi Cao, Chin-Wei Huang, David Yu-Tung Hui, and Joseph Paul Cohen · 2020
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Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John D Owens, and Yixuan Li · 2020
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CSI: novelty detection via contrastive learning on distributionally shifted instances
Jihoon Tack, Sangwoo Mo, Jongheon Jeong, and Jinwoo Shin · 2020
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Detecting out-of-distribution examples with gram matrices
Chandramouli Shama Sastry and Sageev Oore · 2020
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Background data resampling for outlier-aware classification
Yi Li and Nuno Vasconcelos · 2020
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OOD-MAMl: Meta-learning for few-shot out-of-distribution detection and classification
Taewon Jeong and Heeyoung Kim · 2020
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Uncertainty estimation using a single deep deterministic neural network
Joost Van Amersfoort, Lewis Smith, Yee Whye Teh, and Yarin Gal · 2020
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Is out-of-distribution detection learnable?
Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, and Feng Liu · 2022
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Yifei Ming, Ying Fan, and Yixuan Li · 2022
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Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, and Jihun Hamm · 2022
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Out-of-distribution detection with deep nearest neighbors
Yiyou Sun, Yifei Ming, Xiaojin Zhu, and Yixuan Li · 2022
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Provable guarantees for understanding out-of-distribution detection
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How useful are gradients for ood detection really?
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Mitigating neural network overconfidence with logit normalization
Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, and Yixuan Li · 2022
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Breaking down out-of-distribution detection: Many methods based on OOD training data estimate a combination of the same core quantities
Julian Bitterwolf, Alexander Meinke, Maximilian Augustin, and Matthias Hein · 2022
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Adversarial robustness through the lens of causality
Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, and Kun Zhang · 2022
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Scaling out-of-distribution detection for real-world settings
Dan Hendrycks, Steven Basart, Mantas Mazeika, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Song · 2022
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Out-of-distribution detection with implicit outlier transformation
Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, and Bo Han · 2023
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Yadan Luo, Zijian Wang, Zhuoxiao Chen, Zi Huang, and Mahsa Baktashmotlagh · 2023
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Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, and Bo Han · 2023
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How to exploit hyperspherical embeddings for out-of-distribution detection?
Yifei Ming, Yiyou Sun, Ousmane Dia, and Yixuan Li · 2023
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Non-parametric outlier synthesis
Leitian Tao, Xuefeng Du, Xiaojin Zhu, and Yixuan Li · 2023
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