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The ability to detect Out-of-Distribution (OOD) data is important in safety-critical applications of deep learning.
Caltech-256 object category dataset
Gregory Griffin, Alex Holub, and Pietro Perona · 2007
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Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi · 2014
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
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Multi-class texture analysis in colorectal cancer histology
Jakob Nikolas Kather, Cleo-Aron Weis, Francesco Bianconi, Susanne Maria Melchers, Lothar Rudi Schad, Timo Gaiser, Alexander Marx, and Frank G. Zöllner · 2016
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Computer-aided classification of gastrointestinal lesions in regular colonoscopy
Pablo Mesejo, Daniel Pizarro, Armand Abergel, Olivier Y. Rouquette, Sylvain Béorchia, Laurent Poincloux, and Adrien Bartoli · 2016
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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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Openimages: A public dataset for large-scale multi-label and multi-class image classification
Ivan Krasin, Tom Duerig, Neil Alldrin, Vittorio Ferrari, Sami Abu-El-Haija, Alina Kuznetsova, Hassan Rom, Jasper Uijlings, Stefan Popov, Andreas Veit, Serge Belongie, Victor Gomes, Abhinav Gupta, Chen Sun, Gal Chechik, David Cai, Zheyun Feng, Dhyanesh Narayanan, and Kevin Murphy · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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The inaturalist species classification and detection dataset, 2017
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie · 2017
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Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, and Steffen Udluft · 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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Deep ensembles: A loss landscape perspective
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas G. Dietterich · 2019
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Do deep generative models know what they don’t know?
Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, 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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Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data
A unified benchmark for the unknown detection capability of deep neural networks
Jihyo Kim, Jiin Koo, and Sangheum Hwang · 2021
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Uncertainty estimation in autoregressive structured prediction
Andrey Malinin and Mark John Francis Gales · 2021
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Jishnu Mukhoti, Andreas Kirsch, Joost R. van Amersfoort, Philip H. S. Torr, and Yarin Gal · 2021
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Understanding softmax confidence and uncertainty
Tim Pearce, Alexandra Brintrup, and Jun Zhu · 2021
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Scaling ensemble distribution distillation to many classes with proxy targets
Max Ryabinin, Andrey Malinin, and Mark John Francis Gales · 2021
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Yen-Chang Hsu, Yilin Shen, Hongxia Jin, and Zsolt Kira · 2020
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Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John Douglas Owens, and Yixuan Li · 2020
Cited alongside, same era.
Ensemble distribution distillation
Andrey Malinin, Bruno Mlodozeniec, and Mark John Francis Gales · 2020
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Hyperparameter-free out-of-distribution detection using cosine similarity
Engkarat Techapanurak, Masanori Suganuma, and Takayuki Okatani · 2020
Cited alongside, same era.
Exploring the limits of out-of-distribution detection
Stanislav Fort, Jie Ren, and Balaji Lakshminarayanan · 2021
Cited alongside, same era.
Natural adversarial examples
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Xiaodong Song · 2021
Cited alongside, same era.
Mos: Towards scaling out-of-distribution detection for large semantic space
Rui Huang and Yixuan Li · 2021
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Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
Eyke Hüllermeier and Willem Waegeman · 2021
Cited alongside, same era.
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React: Out-of-distribution detection with rectified activations
Yiyou Sun, Chuan Guo, and Yixuan Li · 2021
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Deep ensembles as approximate bayesian inference, Oct 2021
Andrew Gordon Wilson and Pavel Izmailov · 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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On the out-of-distribution generalization of probabilistic image modelling
Mingtian Zhang, Andi Zhang, and Steven G. McDonagh · 2021
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Deep ensembles work, but are they necessary?
Taiga Abe, E. Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, and John P. Cunningham · 2022
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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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Vim: Out-of-distribution with virtual-logit matching
Haoqi Wang, Zhizhong Li, Litong Feng, and Wayne Zhang · 2022
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