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Recent work has shown that deep generative models can assign higher likelihood to out-of-distribution data sets than to their training data (Nalisnick et al., 2019; Choi et al., 2019).
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Concentration and Goodness-of-Fit in Higher Dimensions: (Asymptotically) Distribution-Free Methods
Wolfgang Polonik · 1999
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John C Platt, John Shawe-Taylor, Alex J Smola, Robert C Williamson, et al · 2001
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Clayton D Scott and Robert D Nowak · 2006
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Larry Wasserman · 2006
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Elements of Information Theory
Thomas M Cover and Joy A Thomas · 2012
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Density Estimation Using Real NVP
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Demystifying MMD GANs
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Mass Volume Curves and Anomaly Ranking
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Glow: Generative Flow with Invertible 1x1 Convolutions
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Distribution Matching in Variational Inference
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Does Your Model Know the Digit 6 Is Not a Cat? A Less Biased Evaluation of “Outlier” Detectors
Alireza Shafaei, Mark Schmidt, and James J Little · 2018
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Are Generative Deep Models for Novelty Detection Truly Better?
Vít Škvára, Tomáš Pevnỳ, and Václav Šmídl · 2018
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High-Dimensional Probability: An Introduction with Applications in Data Science , volume 47
Roman Vershynin · 2018
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A Nonparametric Normality Test for High-dimensional Data
Hao Chen and Yin Xia · 2019
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WAIC, but Why?: Generative Ensembles for Robust Anomaly Detection
Hyunsun Choi, Eric Jang, and Alexander Alemi · 2019
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Deep Anomaly Detection with Outlier Exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2019
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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 · 2019
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Likelihood Ratios for Out-of-Distribution Detection
Jie Ren, Peter J Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A DePristo, Joshua V Dillon, and Balaji Lakshminarayanan · 2019
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Data Discovery and Anomaly Detection Using Atypicality for Real-Valued Data
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