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Determining whether inputs are out-of-distribution (OOD) is an essential building block for safely deploying machine learning models in the open world.
A learning algorithm for Boltzmann machines
David H. Ackley, Geoffrey E. Hinton, and Terrence J. Sejnowski · 1985
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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, Marc’Aurelio Ranzato, and Fu-Jie Huang · 2006
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Efficient learning of sparse representations with an energy-based model
Marc’Aurelio Ranzato, Christopher Poultney, Sumit Chopra, and Yann LeCun · 2007
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A unified energy-based framework for unsupervised learning
Marc’Aurelio Ranzato, Y-Lan Boureau, Sumit Chopra, and Yann LeCun · 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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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Efficient learning of deep Boltzmann machines
Ruslan Salakhutdinov and Hugo Larochelle · 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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Auto-encoding variational Bayes
Diederik P. Kingma and Max Welling · 2013
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A family of nonparametric density estimation algorithms
Esteban G Tabak and Cristina V Turner · 2013
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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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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Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 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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SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Conditional image generation with PixelCNN decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, and Koray Kavukcuoglu · 2016
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A theory of generative convnet
Jianwen Xie, Yang Lu, Song-Chun Zhu, and Yingnian Wu · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Predictive uncertainty estimation via prior networks
Andrey Malinin and Mark Gales · 2018
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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 · 2018
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Cooperative training of descriptor and generator networks
Jianwen Xie, Yang Lu, Ruiqi Gao, Song-Chun Zhu, and Ying Nian Wu · 2018
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Learning descriptor networks for 3d shape synthesis and analysis
Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, and Ying Nian Wu · 2018
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Implicit generation and generalization in energy-based models
Yilun Du and Igor Mordatch · 2019
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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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Confidence estimation in deep neural networks via density modelling
Akshayvarun Subramanya, Suraj Srinivas, and R. Venkatesh Babu · 2017
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Synthesizing dynamic patterns by spatial-temporal generative convnet
Jianwen Xie, Song-Chun Zhu, and Ying Nian Wu · 2017
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Energy-based generative adversarial networks
Junbo Zhao, Michael Mathieu, and Yann LeCun · 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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Discriminative out-of-distribution detection for semantic segmentation
Petra Bevandić, Ivan Krešo, Marin Oršić, and Siniša Šegvić · 2018
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Yonatan Geifman and Ran El-Yaniv · 2019
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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
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Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 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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Learning energy-based spatial-temporal generative convnets for dynamic patterns
Jianwen Xie, Song-Chun Zhu, and Ying Nian Wu · 2019
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Informative outlier matters: Robustifying out-of-distribution detection using outlier mining
Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, and Somesh Jha · 2020
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Your classifier is secretly an energy based model and you should treat it like one
Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 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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Self-supervised learning for generalizable out-of-distribution detection
Sina Mohseni, Mandar Pitale, JBS Yadawa, and Zhangyang Wang · 2020
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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 · 2020
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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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Mood: Multi-level out-of-distribution detection
Ziqian Lin, Sreya Dutta, and Yixuan Li · 2021
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