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Deep generative models trained by maximum likelihood remain very popular methods for reasoning about data probabilistically.
Detecting out-of-distribution inputs to deep generative models using typicality
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, and Balaji Lakshminarayanan · 1906
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Novelty detection and neural network validation
Christopher M Bishop · 1994
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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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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and F Huang · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory
Sumio Watanabe and Manfred Opper · 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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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Semi-supervised learning with deep generative models
Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
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Stochastic backpropagation and variational inference in deep latent gaussian models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Linear dynamical neural population models through nonlinear embeddings
Yuanjun Gao, Evan W Archer, Liam Paninski, and John P Cunningham · 2016
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Nonparametric k-nearest-neighbor entropy estimator
Damiano Lombardi and Sanjay Pant · 2016
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Waic, but why? generative ensembles for robust anomaly detection
Hyunsun Choi, Eric Jang, and Alexander A Alemi · 2018
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Parallel wavenet: Fast high-fidelity speech synthesis
Aaron Oord, Yazhe Li, Igor Babuschkin, Karen Simonyan, Oriol Vinyals, Koray Kavukcuoglu, George Driessche, Edward Lockhart, Luis Cobo, Florian Stimberg, et al · 2018
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Why normalizing flows fail to detect out-of-distribution data
Polina Kirichenko, Pavel Izmailov, and Andrew G Wilson · 2020
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Normalizing flows: An introduction and review of current methods
Ivan Kobyzev, Simon Prince, and Marcus Brubaker · 2020
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Perfect density models cannot guarantee anomaly detection
Charline Le Lan and Laurent Dinh · 2020
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Detecting out-of-distribution examples with gram matrices
Chandramouli Shama Sastry and Sageev Oore · 2020
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Understanding anomaly detection with deep invertible networks through hierarchies of distributions and features
Robin Schirrmeister, Yuxuan Zhou, Tonio Ball, and Dan Zhang · 2020
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Input complexity and out-of-distribution detection with likelihood-based generative models
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Yilun Du and Igor Mordatch · 2019
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John Just and Sambuddha Ghosal · 2019
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 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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Practical lossless compression with latent variables using bits back coding
James Townsend, Thomas Bird, and David Barber · 2019
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Flows for simultaneous manifold learning and density estimation
Johann Brehmer and Kyle Cranmer · 2020
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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
Cited in the paper.
Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F Núñez, and Jordi Luque · 2020
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Nvae: A deep hierarchical variational autoencoder
Arash Vahdat and Jan Kautz · 2020
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Likelihood regret: An out-of-distribution detection score for variational auto-encoder
Zhisheng Xiao, Qing Yan, and Yali Amit · 2020
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Rectangular flows for manifold learning
Anthony L Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, and John P Cunningham · 2021
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Density of states estimation for out of distribution detection
Warren Morningstar, Cusuh Ham, Andrew Gallagher, Balaji Lakshminarayanan, Alex Alemi, and Joshua Dillon · 2021
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Understanding failures in out-of-distribution detection with deep generative models
Lily Zhang, Mark Goldstein, and Rajesh Ranganath · 2021
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