SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Hongseok Namkoong and John C. Duchi. Stochastic gradient methods for distributionally robust optimization with f-divergences. In NIPS, 2016.
Paul Michel, Tatsunori Hashimoto, and Graham Neubig. Modeling the second player in distributionally robust optimization. In ICLR, 2021.
Matthew Staib and Stefanie Jegelka. Distributionally robust optimization and generalization in kernel methods. In NeurIPS, 2019.
Rui Dai, Yonggang Zhang, Zhen Fang, Bo Han, and Xinmei Tian. Moderately distributional exploration for domain generalization. In ICML, 2023.
Jose H. Blanchet and M. KarthyekRajhaaA. Quantifying distributional model risk via optimal transport. Risk Management eJournal, 2016.
Alex Krizhevsky and Geoffrey Hinton. Learning multiple layers of features from tiny images. Technical Report TR-2009, University of Toronto, 2009.
Antonio Torralba, Rob Fergus, and William T Freeman. 80 million tiny images: A large data set for nonparametric object and scene recognition. IEEE transactions on pattern analysis and machine intelligence, 30(11):1958-1970, 2008.
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. Describing textures in the wild. In  $CVPR$ , 2014.
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng. Reading digits in natural images with unsupervised feature learning. In NIPS Workshop, 2011.
Bolei Zhou, Ågata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba. Places: A 10 million image database for scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6):1452-1464, 2018.
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao. LSUN: construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365, 2015.
Pingmei Xu, Krista A Ehinger, Yinda Zhang, Adam Finkelstein, Sanjeev R Kulkarni, and Jianxiong Xiao. Turkergaze: crowdsourcing saliency with webcam based eye tracking. arXiv preprint arXiv:1504.06755, 2015.
Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. In BMVC, 2016.
Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradient descent with warm restarts. In *ICLR*, 2017.
Andrija Djurisic, Nebojsa Bozanic, Arjun Ashok, and Rosanne Liu. Extremely simple activation shaping for out-of-distribution detection. In ICLR, 2023.
Yiyou Sun, Chuan Guo, and Yixuan Li. ReAct: out-of-distribution detection with rectified activations. In NeurIPS, 2021.
Jihoon Tack, Sangwoo Mo, Jongheon Jeong, and Jinwoo Shin. CSI: novelty detection via contrastive learning on distributionally shifted instances. In NeurIPS, 2020.
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: a large-scale hierarchical image database. In CVPR, 2009.
Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-SNE. Journal of machine learning research, 9(11):2579-2605, 2008.
Shiyu Liang, Yixuan Li, and R. Srikant. Enhancing the reliability of out-of-distribution image detection in neural networks. In ICLR, 2018.
Haoran Wang, Weitang Liu, Alex Bocchieri, and Yixuan Li. Can multi-label classification networks know what they don't know? In NeurIPS, 2021a.
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. In NIPS, 2017.
Rui Huang and Yixuan Li. MOS: towards scaling out-of-distribution detection for large semantic space. In CVPR, 2021.
Chandramouli Shama Sastry and Sageev Oore. Detecting out-of-distribution examples with gram matrices. In ICML, 2020.
Haoqi Wang, Zhizhong Li, Litong Feng, and Wayne Zhang. ViM: Out-of-distribution with virtual-logit matching. In CVPR, 2022a.
Ziqian Lin, Sreya Dutta Roy, and Yixuan Li. MOOD: Multi-level out-of-distribution detection. In CVPR, 2021.
Peyman Morteza and Yixuan Li. Provable guarantees for understanding out-of-distribution detection. In AAAI, 2022.
Yadan Luo, Zijian Wang, Zhuoxiao Chen, Zi Huang, and Mahsa Baktashmotlagh. Source-free progressive graph learning for open-set domain adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(9):11240-11255, 2023.
Rui Huang, Andrew Geng, and Yixuan Li. On the importance of gradients for detecting distributional shifts in the wild. In NeurIPS, 2021.
Conor Igoe, Youngseog Chung, Ian Char, and Jeff Schneider. How useful are gradients for ood detection really? arXiv preprint arXiv:2205.10439, 2022.
Jianing Zhu, Hengzhuang Li, Jiangchao Yao, Tongliang Liu, Jianliang Xu, and Bo Han. Unleashing mask: Explore the intrinsic out-of-distribution detection capability. In ICML, 2023a.
Vikash Sehwag, Mung Chiang, and Prateek Mittal. SSD: A unified framework for self-supervised outlier detection. In ICLR, 2021.
Haotao Wang, Aston Zhang, Yi Zhu, Shuai Zheng, Mu Li, Alex J Smola, and Zhangyang Wang. Partial and asymmetric contrastive learning for out-of-distribution detection in long-tailed recognition. In ICML, 2022b.
Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, and Bo Han. Out-of-distribution detection learning with unreliable out-of-distribution sources. In NeurIPS, 2023.
Xuefeng Du, Gabriel Gozum, Yifei Ming, and Yixuan Li. SIREN: Shaping representations for detecting out-of-distribution objects. In NeurIPS, 2022b.
Yifei Ming, Yiyou Sun, Ousmane Dia, and Yixuan Li. How to exploit hyperspherical embeddings for out-of-distribution detection? In ICLR, 2023.
Alireza Zaeemzadeh, Niccolò Bisagno, Zeno Sambugaro, Nicola Concí, Nazanin Rahnavard, and Mubarak Shah. Out-of-distribution detection using union of 1-dimensional subspaces. In CVPR, 2021.
Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, and Yixuan Li. Mitigating neural network overconfidence with logit normalization. In ICML, 2022.
Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, and Tongliang Liu. Harnessing out-of-distribution examples via augmenting content and style. In ICLR, 2023a.
Jianing Zhu, Geng Yu, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, and Bo Han. Diversified outlier exposure for out-of-distribution detection via informative extrapolation. In NeurIPS, 2023b.
Yi Li and Nuno Vasconcelos. Background data resampling for outlier-aware classification. In CVPR, 2020.