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In safety-critical applications of machine learning, it is often desirable for a model to be conservative, abstaining from making predictions on unknown inputs which are not well-represented in the training data.
Information and information stability of random variables and processes
Mark S Pinsker · 1964
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The nearest neighbor classification rule with a reject option
Martin E. Hellman · 1970
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Support vector machines with embedded reject option
Giorgio Fumera and Fabio Roli · 2002
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80 million tiny images: A large data set for nonparametric object and scene recognition
Antonio Torralba, Rob Fergus, and William T. Freeman · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, R. Socher, Li Fei-Fei, Wei Dong, Kai Li, and Li-Jia Li · 2009
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Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A. Ehinger, Aude Oliva, and Antonio Torralba · 2010
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Describing textures in the wild, 2013
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Tiny imagenet visual recognition challenge
Ya Le and Xuan S. Yang · 2015
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Rethinking the inception architecture for computer vision, 2015
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2015
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Turkergaze: Crowdsourcing saliency with webcam based eye tracking, 2015
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, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao · 2015
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Towards open set deep networks
Abhijit Bendale and Terrance E Boult · 2016
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Boosting with abstention
Corinna Cortes, Giulia DeSalvo, and Mehryar Mohri · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Wide residual networks, 2016
Sergey Zagoruyko and Nikos Komodakis · 2016
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Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 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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Training confidence-calibrated classifiers for detecting out-of-distribution samples
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin · 2017
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Deep feature interpolation for image content changes
Paul Upchurch, Jacob Gardner, Geoff Pleiss, Robert Pless, Noah Snavely, Kavita Bala, and Kilian Weinberger · 2017
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Understanding deep learning requires rethinking generalization
C Zhang, S Bengio, M Hardt, B Recht, and O Vinyals · 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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From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge
Peter Bandi, Oscar Geessink, Quirine Manson, Marcory Van Dijk, Maschenka Balkenhol, Meyke Hermsen, Babak Ehteshami Bejnordi, Byungjae Lee, Kyunghyun Paeng, Aoxiao Zhong, et al · 2018
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Learning confidence for out-of-distribution detection in neural networks
Terrance DeVries and Graham W Taylor · 2018
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An image is worth 16x16 words: Transformers for image recognition at scale, 2021
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 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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Selective classification via one-sided prediction
Aditya Gangrade, Anil Kag, and Venkatesh Saligrama · 2021
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On the importance of gradients for detecting distributional shifts in the wild, 2021
Rui Huang, Andrew Geng, and Yixuan Li · 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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Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2018
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The inaturalist species classification and detection dataset, 2018
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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A simple unified framework for detecting out-of-distribution samples and adversarial attacks, 2018
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
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Addressing failure prediction by learning model confidence
Charles Corbière, Nicolas Thome, Avner Bar-Hen, Matthieu Cord, and Patrick Pérez · 2019
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Selective prediction-set models with coverage guarantees
Jean Feng, Arjun Sondhi, Jessica Perry, and Noah Simon · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Deep gamblers: Learning to abstain with portfolio theory
Ziyin Liu, Zhikang Wang, Paul Pu Liang, Russ R Salakhutdinov, Louis-Philippe Morency, and Masahito Ueda · 2019
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Measuring calibration in deep learning
Jeremy Nixon, Michael W Dusenberry, Linchuan Zhang, Ghassen Jerfel, and Dustin Tran · 2019
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Jie Ren, Stanislav Fort, Jeremiah Z. Liu, Abhijit Guha Roy, Shreyas Padhy, and Balaji Lakshminarayanan · 2021
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Extending the wilds benchmark for unsupervised adaptation
Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, et al · 2021
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No true state-of-the-art? ood detection methods are inconsistent across datasets
Fahim Tajwar, Ananya Kumar, Sang Michael Xie, and Percy Liang · 2021
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A short note on an inequality between kl and tv, 2022
Clément L. Canonne · 2022
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Calibrated selective classification
Adam Fisch, Tommi Jaakkola, and Regina Barzilay · 2022
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Scaling out-of-distribution detection for real-world settings
Dan Hendrycks, Steven Basart, Mantas Mazeika, Andy Zou, Joseph Kwon, Mohammadreza Mostajabi, Jacob Steinhardt, and Dawn Song · 2022
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A call to reflect on evaluation practices for failure detection in image classification
Paul F Jaeger, Carsten T Lüth, Lukas Klein, and Till J Bungert · 2022
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Training OOD detectors in their natural habitats
Julian Katz-Samuels, Julia B Nakhleh, Robert Nowak, and Yixuan Li · 2022
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Delving into out-of-distribution detection with vision-language representations
Yifei Ming, Ziyang Cai, Jiuxiang Gu, Yiyou Sun, Wei Li, and Yixuan Li · 2022
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Adversarial unlearning: Reducing confidence along adversarial directions
Amrith Setlur, Benjamin Eysenbach, Virginia Smith, and Sergey Levine · 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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Semi-supervised novelty detection using ensembles with regularized disagreement
Alexandru Tifrea, Eric Stavarache, and Fanny Yang · 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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Vim: Out-of-distribution with virtual-logit matching
Haoqi Wang, Zhizhong Li, Litong Feng, and Wayne Zhang · 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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Augmenting softmax information for selective classification with out-of-distribution data
Guoxuan Xia and Christos-Savvas Bouganis · 2022
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Dream the impossible: Outlier imagination with diffusion models, 2023
Xuefeng Du, Yiyou Sun, Xiaojin Zhu, and Yixuan Li · 2023
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Plugin estimators for selective classification with out-of-distribution detection, 2023
Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, and Sanjiv Kumar · 2023
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Non-parametric outlier synthesis
Leitian Tao, Xuefeng Du, Jerry Zhu, and Yixuan Li · 2023
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