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Thanks to the tractability of their likelihood, several deep generative models show promise for seemingly straightforward but important applications like anomaly detection, uncertainty estimation, and active learning.
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In High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting
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Data and its (dis) contents: A survey of dataset development and use in machine learning research
Paullada, A.; Raji, I.D.; Bender, E.M.; Denton, E.; Hanna, A · 2012
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Generating sequences with recurrent neural networks
Graves, A · 2013
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Representation learning: A review and new perspectives
Bengio, Y.; Courville, A.; Vincent, P · 2013
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A family of nonparametric density estimation algorithms
Tabak, E.G.; Turner, C.V · 2013
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Vershynin, R · 2018
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Gueguen, L.; Sergeev, A.; Kadlec, B.; Liu, R.; Yosinski, J · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J.; Gebru, T · 2018
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Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Hendrycks, D.; Dietterich, T · 2019
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Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
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Do Deep Generative Models Know What They Don’t Know?
Nalisnick, E.; Matsukawa, A.; Teh, Y.W.; Gorur, D.; Lakshminarayanan, B · 2019
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Deep Anomaly Detection with Outlier Exposure
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Challenging common assumptions in the unsupervised learning of disentangled representations
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de Vries, T.; Misra, I.; Wang, C.; van der Maaten, L · 2019
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Implicit generation and modeling with energy based models
Du, Y.; Mordatch, I · 2019
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Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Filos, A.; Tigkas, P.; Mcallister, R.; Rhinehart, N.; Levine, S.; Gal, Y · 2020
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Normalizing flows: An introduction and review of current methods
Kobyzev, I.; Prince, S.; Brubaker, M · 2020
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Understanding the Limitations of Conditional Generative Models
Fetaya, E.; Jacobsen, J.H.; Grathwohl, W.; Zemel, R · 2020
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Why Normalizing Flows Fail to Detect Out-of-Distribution Data
Kirichenko, P.; Izmailov, P.; Wilson, A.G · 2020
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Hybrid Models for Open Set Recognition
Zhang, H.; Li, A.; Guo, J.; Guo, Y · 2020
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Further Analysis of Outlier Detection with Deep Generative Models
Wang, Z.; Dai, B.; Wipf, D.; Zhu, J · 2020
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Musings on typicality. https://benanne.github.io/2020/09/01/typicality.html . 2020
Dieleman, S · 2020
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Raji, D.I.; Denton, E.; Hanna, A.; Bender, E.M.; Paullada, A · 2020
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Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models
Serrà, J.; Álvarez, D.; Gómez, V.; Slizovskaia, O.; Núñez, J.F.; Luque, J · 2020
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Towards a critical race methodology in algorithmic fairness
Hanna, A.; Denton, E.; Smart, A.; Smith-Loud, J · 2020
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Neural machine translation with byte-level subwords
Wang, C.; Cho, K.; Gu, J · 2020
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Your classifier is secretly an energy based model and you should treat it like one
Grathwohl, W.; Wang, K.C.; Jacobsen, J.H.; Duvenaud, D.; Norouzi, M.; Swersky, K · 2020
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Lessons from the PULSE Model and Discussion
Kurenkov, A · 2020
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Harris, C.R.; Millman, K.J.; van der Walt, S.J.; Gommers, R.; Virtanen, P.; Cournapeau, D.; Wieser, E.; Taylor, J.; Berg, S.; Smith, N.J · 2020
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A unifying review of deep and shallow anomaly detection
Ruff, L.; Kauffmann, J.R.; Vandermeulen, R.A.; Montavon, G.; Samek, W.; Kloft, M.; Dietterich, T.G.; Müller, K.R · 2021
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Normalizing Flows for Probabilistic Modeling and Inference
Papamakarios, G.; Nalisnick, E.; Rezende, D.J.; Mohamed, S.; Lakshminarayanan, B · 2021
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Density of States Estimation for Out of Distribution Detection
Morningstar, W.; Ham, C.; Gallagher, A.; Lakshminarayanan, B.; Alemi, A.; Dillon, J · 2021
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Understanding and Mitigating Exploding Inverses in Invertible Neural Networks
Behrmann, J.; Vicol, P.; Wang, K.C.; Grosse, R.; Jacobsen, J.H · 2021
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Understanding Failures in Out-of-Distribution Detection with Deep Generative Models
Zhang, L.; Goldstein, M.; Ranganath, R · 2021
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Large Image Datasets: A Pyrrhic Win for Computer Vision?
Birhane, A.; Prabhu, V.U · 2021
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