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An intuitive way to detect out-of-distribution (OOD) data is via the density function of a fitted probabilistic generative model: points with low density may be classed as OOD.
Detecting out-of-distribution inputs to deep generative models using a test for typicality
E. Nalisnick, A. Matsukawa, Y. W. Teh, and B. Lakshminarayanan · 1906
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Ix. on the problem of the most efficient tests of statistical hypotheses
J. Neyman and E. S. Pearson · 1933
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Novelty detection and neural network validation
C. M. Bishop · 1994
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
D. Hendrycks and K. Gimpel · 2016
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Enhancing the reliability of out-of-distribution image detection in neural networks
S. Liang, Y. Li, and R. Srikant · 2017
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Deep anomaly detection with outlier exposure
D. Hendrycks, M. Mazeika, and T. Dietterich · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
K. Lee, K. Lee, H. Lee, and J. Shin · 2018
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Predictive uncertainty estimation via prior networks
A. Malinin and M. Gales · 2018
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Evidential deep learning to quantify classification uncertainty
M. Sensoy, L. Kaplan, and M. Kandemir · 2018
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Likelihood ratios for out-of-distribution detection
J. Ren, P. J. Liu, E. Fertig, J. Snoek, R. Poplin, M. Depristo, J. Dillon, and B. Lakshminarayanan · 2019
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Input complexity and out-of-distribution detection with likelihood-based generative models
Foundations of data science
A. Blum, J. Hopcroft, and R. Kannan · 2020
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Detecting out-of-distribution examples with gram matrices
C. S. Sastry and S. Oore · 2020
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Understanding anomaly detection with deep invertible networks through hierarchies of distributions and features
R. Schirrmeister, Y. Zhou, T. Ball, and D. Zhang · 2020
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A unifying review of deep and shallow anomaly detection
L. Ruff, J. R. Kauffmann, R. A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T. G. Dietterich, and K.-R. Müller · 2021
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On the out-of-distribution generalization of probabilistic image modelling
M. Zhang, A. Zhang, and S. McDonagh · 2021
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J. Serrà, D. Álvarez, V. Gómez, O. Slizovskaia, J. F. Núñez, and J. Luque · 2019
Cited alongside, same era.
Do deep generative models know what they don’t know?
E. Nalisnick, A. Matsukawa, Y. W. Teh, D. Gorur, and B. Lakshminarayanan
Cited in the paper.
M. Zhang, A. Zhang, T. Z. Xiao, Y. Sun, and S. McDonagh · 2022
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