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The task of out-of-distribution (OOD) detection is crucial for deploying machine learning models in real-world settings.
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Towards open set deep networks
Abhijit Bendale and Terrance E Boult · 2016
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Conditional image generation with pixelcnn decoders
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Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 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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Xun Huang, Yixuan Li, Omid Poursaeed, John Hopcroft, and Serge Belongie · 2017
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Places: A 10 million image database for scene recognition
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Deep anomaly detection using geometric transformations
Izhak Golan and Ran El-Yaniv · 2018
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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 Rayadurgam Srikant · 2018
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The inaturalist species classification and detection dataset
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Latent space autoregression for novelty detection
Davide Abati, Angelo Porrello, Simone Calderara, and Rita Cucchiara · 2019
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Learning to discover novel visual categories via deep transfer clustering
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
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Exploring the limits of out-of-distribution detection
Stanislav Fort, Jie Ren, and Balaji Lakshminarayanan · 2021
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Adaptive conformal inference under distribution shift
Isaac Gibbs and Emmanuel Candes · 2021
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Mos: 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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Locally most powerful bayesian test for out-of-distribution detection using deep generative models
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Kai Han, Andrea Vedaldi, and Andrew Zisserman · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Augmix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 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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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 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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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 · 2019
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Keunseo Kim, JunCheol Shin, and Heeyoung Kim · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 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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Robust generalization despite distribution shift via minimum discriminating information
Tobias Sutter, Andreas Krause, and Daniel Kuhn · 2021
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Can multi-label classification networks know what they don’t know?
Haoran Wang, Weitang Liu, Alex Bocchieri, and Yixuan Li · 2021
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Generalized out-of-distribution detection: A survey
Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu · 2021
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Towards a theoretical framework of out-of-distribution generalization
Haotian Ye, Chuanlong Xie, Tianle Cai, Ruichen Li, Zhenguo Li, and Liwei Wang · 2021
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Tokens-to-token vit: Training vision transformers from scratch on imagenet
Li Yuan, Yunpeng Chen, Tao Wang, Weihao Yu, Yujun Shi, Zi-Hang Jiang, Francis EH Tay, Jiashi Feng, and Shuicheng Yan · 2021
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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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Neighborhood contrastive learning for novel class discovery
Zhun Zhong, Enrico Fini, Subhankar Roy, Zhiming Luo, Elisa Ricci, and Nicu Sebe · 2021
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Vos: Learning what you don’t know by virtual outlier synthesis
Xuefeng Du, Zhaoning Wang, Mu Cai, and Yixuan Li · 2022
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Leveraging unlabeled data to predict out-of-distribution performance
Saurabh Garg, Sivaraman Balakrishnan, Zachary C Lipton, Behnam Neyshabur, and Hanie Sedghi · 2022
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Igeood: An information geometry approach to out-of-distribution detection
Eduardo Dadalto Camara Gomes, Florence Alberge, Pierre Duhamel, and Pablo Piantanida · 2022
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A statistical framework for efficient out of distribution detection in deep neural networks
Matan Haroush, Tzviel Frostig, Ruth Heller, and Daniel Soudry · 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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Revisiting flow generative models for out-of-distribution detection
Dihong Jiang, Sun Sun, and Yaoliang Yu · 2022
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Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang · 2022
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Open-set recognition: A good closed-set classifier is all you need
Sagar Vaze, Kai Han, Andrea Vedaldi, and Andrew Zisserman · 2022
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A fine-grained analysis on distribution shift
Olivia Wiles, Sven Gowal, Florian Stimberg, Sylvestre Alvise-Rebuffi, Ira Ktena, Taylan Cemgil, et al · 2022
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