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Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications and has thus been extensively studied, with a plethora of methods developed in the literature.
Extreme value theory
Richard L Smith · 1990
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Ensemble methods in machine learning
Thomas G Dietterich · 2000
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One-class classification: Concept learning in the absence of counter-examples
David Martinus Johannes Tax · 2002
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The open world assumption
Nick Drummond and Rob Shearer · 2006
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The black swan: The impact of the highly improbable
Nassim Taleb · 2007
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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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Isolation forest
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou · 2008
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Anomaly detection: A survey
Varun Chandola, Arindam Banerjee, and Vipin Kumar · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Cifar-10 and cifar-100 datasets
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2009
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Notmnist dataset
Yaroslav Bulatov · 2011
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Kylberg texture dataset v. 1.0
Gustaf Kylberg · 2011
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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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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
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Toward open set recognition
Walter J Scheirer, Anderson de Rezende Rocha, Archana Sapkota, and Terrance E Boult · 2013
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Probability models for open set recognition
Walter J Scheirer, Lalit P Jain, and Terrance E Boult · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Towards open set deep networks
Abhijit Bendale and Terrance E Boult · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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Generative openmax for multi-class open set classification
ZongYuan Ge, Sergey Demyanov, Zetao Chen, and Rahil Garnavi · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Earlier work this paper cites.
Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Earlier work this paper cites.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Learning confidence for out-of-distribution detection in neural networks
Terrance DeVries and Graham W Taylor · 2018
Earlier work this paper cites.
Predictive uncertainty estimation via prior networks
Andrey Malinin and Mark Gales · 2018
Cited alongside, same era.
Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and Rayadurgam Srikant · 2018
Cited alongside, same era.
Generative probabilistic novelty detection with adversarial autoencoders
Stanislav Pidhorskyi, Ranya Almohsen, Donald A Adjeroh, and Gianfranco Doretto · 2018
Cited alongside, same era.
Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen · 2018
Cited alongside, same era.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
Cited alongside, same era.
Improving reconstruction autoencoder out-of-distribution detection with mahalanobis distance
A unifying review of deep and shallow anomaly detection
Lukas Ruff, Jacob R Kauffmann, Robert A Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G Dietterich, and Klaus-Robert Müller · 2021
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Adversarial reciprocal points learning for open set recognition
Guangyao Chen, Peixi Peng, Xiangqian Wang, and Yonghong Tian · 2021
Later among the works it cites.
Semantically coherent out-of-distribution detection
Jingkang Yang, Haoqi Wang, Litong Feng, Xiaopeng Yan, Huabin Zheng, Wayne Zhang, and Ziwei Liu · 2021
Later among the works it cites.
Mos: Towards scaling out-of-distribution detection for large semantic space
Rui Huang and Yixuan Li · 2021
Later among the works it cites.
Natural adversarial examples
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 2021
Later among the works it cites.
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Taylor Denouden, Rick Salay, Krzysztof Czarnecki, Vahdat Abdelzad, Buu Phan, and Sachin Vernekar · 2018
Cited alongside, same era.
Open set learning with counterfactual images
Lawrence Neal, Matthew Olson, Xiaoli Fern, Weng-Keen Wong, and Fuxin Li · 2018
Cited alongside, same era.
Deep one-class classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, and Marius Kloft · 2018
Cited alongside, same era.
Latent space autoregression for novelty detection
Davide Abati, Angelo Porrello, Simone Calderara, and Rita Cucchiara · 2019
Cited alongside, same era.
Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2019
Cited alongside, same era.
Unsupervised out-of-distribution detection by maximum classifier discrepancy
Qing Yu and Kiyoharu Aizawa · 2019
Cited alongside, same era.
Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem
Matthias Hein, Maksym Andriushchenko, and Julian Bitterwolf · 2019
Cited alongside, same era.
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler · 2021
Later among the works it cites.
Draem-a discriminatively trained reconstruction embedding for surface anomaly detection
Vitjan Zavrtanik, Matej Kristan, and Danijel Skočaj · 2021
Later among the works it cites.
Opengan: Open-set recognition via open data generation
Shu Kong and Deva Ramanan · 2021
Later among the works it cites.
On the importance of gradients for detecting distributional shifts in the wild
Rui Huang, Andrew Geng, and Yixuan Li · 2021
Later among the works it cites.
React: Out-of-distribution detection with rectified activations
Yiyou Sun, Chuan Guo, and Yixuan Li · 2021
Later among the works it cites.
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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Pixmix: Dreamlike pictures comprehensively improve safety measures
Dan Hendrycks, Andy Zou, Mantas Mazeika, Leonard Tang, Dawn Song, and Jacob Steinhardt · 2021
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Open-vocabulary object detection via vision and language knowledge distillation
Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui · 2021
Later among the works it cites.
Open-vocabulary object detection using captions
Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang · 2021
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X-risk analysis for ai research
Dan Hendrycks and Mantas Mazeika · 2022
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Rethinking reconstruction autoencoder-based out-of-distribution detection
Yibo Zhou · 2022
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Adbench: Anomaly detection benchmark
Songqiao Han, Xiyang Hu, Hailiang Huang, Mingqi Jiang, and Yue Zhao · 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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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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Vim: Out-of-distribution with virtual-logit matching
Haoqi Wang, Zhizhong Li, Litong Feng, and Wayne Zhang · 2022
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Dice: Leveraging sparsification for out-of-distribution detection
Yiyou Sun and Sharon 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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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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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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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus · 2022
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Full-spectrum out-of-distribution detection
Jingkang Yang, Kaiyang Zhou, and Ziwei Liu · 2022
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Unknown-aware object detection: Learning what you don’t know from videos in the wild
Xuefeng Du, Xin Wang, Gabriel Gozum, and Yixuan Li · 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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Simple open-vocabulary object detection with vision transformers
Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, et al · 2022
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