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In this paper, we present a general method that can improve the sample quality of pre-trained likelihood based generative models.
Structural image restoration through deformable template
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Max Welling and Yee Whye Teh · 2011
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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IJ Goodfellow, J Pouget-Abadie, M Mirza, B Xu, D Warde-Farley, S Ozair, A Courville, and Y Bengio · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Conditional image generation with pixelcnn decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
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Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, et al · 2017
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Discriminator rejection sampling, 2018
Samaneh Azadi, Catherine Olsson, Trevor Darrell, Ian Goodfellow, and Augustus Odena · 2018
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Bin Dai and David Wipf · 2019
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Implicit generation and generalization in energy-based models
Yilun Du and Igor Mordatch · 2019
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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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On the anatomy of mcmc-based maximum likelihood learning of energy-based models
Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, and Ying Nian Wu · 2019
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Learning non-convergent non-persistent short-run mcmc toward energy-based model
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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 · 2018
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Metropolis-hastings generative adversarial networks
Ryan Turner, Jane Hung, Eric Frank, Yunus Saatci, and Jason Yosinski · 2018
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Erik Nijkamp, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu · 2019
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Discriminator optimal transport
Akinori Tanaka · 2019
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Generative latent flow: A framework for non-adversarial image generation
Zhisheng Xiao, Qing Yan, Yi’an Chen, and Yali Amit · 2019
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Your gan is secretly an energy-based model and you should use discriminator driven latent sampling
Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, and Yoshua Bengio · 2020
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Augmented normalizing flows: Bridging the gap between generative flows and latent variable models
Chin-Wei Huang, Laurent Dinh, and Aaron Courville · 2020
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