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Deep probabilistic generative models enable modeling the likelihoods of very high dimensional data.
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Deep learning face attributes in the wild
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
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Concrete problems in ai safety
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Wavenet: A generative model for raw audio
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Anomaly detection with generative adversarial networks for multivariate time series
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Do deep generative models know what they don’t know?
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Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications
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Ganomaly: Semi-supervised anomaly detection via adversarial training
Samet Akcay, Amir Atapour-Abarghouei, and Toby P Breckon · 2018
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Understanding disentangling in β \beta -vae. arxiv 2018
Christopher P Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, and Alexander Lerchner · 2018
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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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Deep learning for classical japanese literature
Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, and David Ha · 2018
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Negative sampling in variational autoencoders
Adrián Csiszárik, Beatrix Benkő, and Dániel Varga · 2019
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Diagnosing and enhancing vae models
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Your classifier is secretly an energy based model and you should treat it like one
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 2019
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Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, 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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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 · 2019
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Unsupervised out-of-distribution detection with batch normalization
Jiaming Song, Yang Song, and Stefano Ermon · 2019
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Zhisheng Xiao, Qing Yan, and Yali Amit · 2019
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Time series anomaly detection with variational autoencoders
Chunkai Zhang and Yingyang Chen · 2019
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Anomalous instance detection in deep learning: A survey
Saikiran Bulusu, Bhavya Kailkhura, Bo Li, Pramod K Varshney, and Dawn Song · 2020
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Iterative energy-based projection on a normal data manifold for anomaly localization
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