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Out-of-distribution (OOD) detection is a critical task for reliable machine learning.
Directional statistics , volume 2
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Imagenet: A large-scale hierarchical image database
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Learning multiple layers of features from tiny images
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Sun database: Large-scale scene recognition from abbey to zoo
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Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
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Turkergaze: Crowdsourcing saliency with webcam based eye tracking
Pingmei Xu, Krista A Ehinger, Yinda Zhang, Adam Finkelstein, Sanjeev R Kulkarni, and Jianxiong Xiao · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
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Towards open set deep networks
Abhijit Bendale and Terrance E Boult · 2016
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Deep metric learning via lifted structured feature embedding
Hyun Oh Song, Yu Xiang, Stefanie Jegelka, and Silvio Savarese · 2016
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Beyond triplet loss: a deep quadruplet network for person re-identification
Weihua Chen, Xiaotang Chen, Jianguo Zhang, and Kaiqi Huang · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
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Sphereface: Deep hypersphere embedding for face recognition
Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, and Le Song · 2017
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No fuss distance metric learning using proxies
Yair Movshovitz-Attias, Alexander Toshev, Thomas K Leung, Sergey Ioffe, and Saurabh Singh · 2017
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Joint detection and identification feature learning for person search
Tong Xiao, Shuang Li, Bochao Wang, Liang Lin, and Xiaogang Wang · 2017
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 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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Umap: Uniform manifold approximation and projection
Leland McInnes, John Healy, Nathaniel Saul, and Lukas Grossberger · 2018
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The inaturalist species classification and detection dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie · 2018
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Cosface: Large margin cosine loss for deep face recognition
Hao Wang, Yitong Wang, Zheng Zhou, Xing Ji, Dihong Gong, Jingchao Zhou, Zhifeng Li, and Wei Liu · 2018
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Arcface: Additive angular margin loss for deep face recognition
Input complexity and out-of-distribution detection with likelihood-based generative models
Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F. Núñez, and Jordi Luque · 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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Proxynca++: Revisiting and revitalizing proxy neighborhood component analysis
Eu Wern Teh, Terrance DeVries, and Graham W Taylor · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
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Contrastive training for improved out-of-distribution detection
Jim Winkens, Rudy Bunel, Abhijit Guha Roy, Robert Stanforth, Vivek Natarajan, Joseph R Ledsam, Patricia MacWilliams, Pushmeet Kohli, Alan Karthikesalingam, Simon Kohl, et al · 2020
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Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou · 2019
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Deep metric learning beyond binary supervision
Sungyeon Kim, Minkyo Seo, Ivan Laptev, Minsu Cho, and Suha Kwak · 2019
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Do deep generative models know what they don’t know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2019
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Likelihood ratios for out-of-distribution detection
Jie Ren, Peter J Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark Depristo, Joshua Dillon, and Balaji Lakshminarayanan · 2019
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Representation learning with contrastive predictive coding, 2019
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2019
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data
Yen-Chang Hsu, Yilin Shen, Hongxia Jin, and Zsolt Kira · 2020
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Likelihood regret: An out-of-distribution detection score for variational auto-encoder
Zhisheng Xiao, Qing Yan, and Yali Amit · 2020
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Distance-based learning from errors for confidence calibration
Chen Xing, Sercan Arik, Zizhao Zhang, and Tomas Pfister · 2020
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Semi-supervised learning of visual features by non-parametrically predicting view assignments with support samples
Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Armand Joulin, Nicolas Ballas, and Michael Rabbat · 2021
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Towards scaling out-of-distribution detection for large semantic space
Rui Huang and Yixuan Li · 2021
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On the importance of gradients for detecting distributional shifts in the wild
Rui Huang, Andrew Geng, and Yixuan Li · 2021
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Contrastive learning with hard negative samples
Joshua David Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka · 2021
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Ssd: A unified framework for self-supervised outlier detection
Vikash Sehwag, Mung Chiang, and Prateek Mittal · 2021
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React: Out-of-distribution detection with rectified activations
Yiyou Sun, Chuan Guo, and Yixuan Li · 2021
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Step: Out-of-distribution detection in the presence of limited in-distribution labeled data
Zhi Zhou, Lan-Zhe Guo, Zhanzhan Cheng, Yu-Feng Li, and Shiliang Pu · 2021
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Distance-based hyperspherical classification for multi-source open-set domain adaptation
Silvia Bucci, Francesco Cappio Borlino, Barbara Caputo, and Tatiana Tommasi · 2022
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Siren: Shaping representations for detecting out-of-distribution objects
Xuefeng Du, Gabriel Gozum, Yifei Ming, and Yixuan Li · 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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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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