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In recent years, deep generative models have attracted increasing interest due to their capacity to model complex distributions.
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Yann LeCun · 1998
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Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul · 1999
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Multiscale structural similarity for image quality assessment
Zhou Wang, Eero P Simoncelli, and Alan C Bovik · 2003
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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Hamiltonian importance sampling
Radford M Neal · 2005
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Adam Coates, Andrew Ng, and Honglak Lee · 2011
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Scikit-learn: Machine learning in python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Diederik P Kingma and Jimmy Ba · 2014
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 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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Made: Masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Markov chain monte carlo and variational inference: Bridging the gap
Tim Salimans, Diederik Kingma, and Max Welling · 2015
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Modeling and transforming speech using variational autoencoders
Merlijn Blaauw and Jordi Bonada · 2016
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Importance weighted autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2016
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Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Elbo surgery: yet another way to carve up the variational evidence lower bound
Matthew D Hoffman and Matthew J Johnson · 2016
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Improved variational inference with inverse autoregressive flow
Durk P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 2016
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Alireza et al. Makhzani · 2016
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Approximate inference for deep latent gaussian mixtures
Eric Nalisnick, Lars Hertel, and Padhraic Smyth · 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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Ladder variational autoencoder
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, and Ole Winther · 2016
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Infovae: Information maximizing variational autoencoders
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2016
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Deep variational information bottleneck
The riemannian geometry of deep generative models
Hang Shao, Abhishek Kumar, and P. Thomas Fletcher · 2018
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How good is my gan?
Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari · 2018
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Wasserstein auto-encoders
I Tolstikhin, O Bousquet, S Gelly, and B Schölkopf · 2018
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Vae with a vampprior
Jakub Tomczak and Max Welling · 2018
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Advances in variational inference
Cheng Zhang, Judith Bütepage, Hedvig Kjellström, and Stephan Mandt · 2018
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Understanding and improving interpolation in autoencoders via an adversarial regularizer
David Berthelot*, Colin Raffel*, Aurko Roy, and Ian Goodfellow · 2019
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Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2017
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Deep unsupervised clustering with gaussian mixture variational autoencoders
Nat Dilokthanakul, Pedro A. M. Mediano, Marta Garnelo, Matthew C. H. Lee, Hugh Salimbeni, Kai Arulkumaran, and Murray Shanahan · 2017
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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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beta-VAE: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Masked autoregressive flow for density estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Learning to generate images with perceptual similarity metrics
Jake Snell, Karl Ridgeway, Renjie Liao, Brett D Roads, Michael C Mozer, and Richard S Zemel · 2017
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Ekaba Bisong · 2019
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Learning Hierarchical Priors in VAEs
Alexej Klushyn, Nutan Chen, Richard Kurle, and Botond Cseke · 2019
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Deep clustering by gaussian mixture variational autoencoders with graph embedding
Linxiao Yang, Ngai-Man Cheung, Jiaying Li, and Jun Fang · 2019
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NCP-VAE: Variational autoencoders with noise contrastive priors
Jyoti Aneja, Alexander Schwing, Jan Kautz, and Arash Vahdat · 2020
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Experiment tracking with weights and biases, 2020
Lukas Biewald · 2020
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Effectively unbiased fid and inception score and where to find them
Min Jin Chong and David Forsyth · 2020
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From variational to deterministic autoencoders
Partha Ghosh, Mehdi SM Sajjadi, Antonio Vergari, Michael Black, and Bernhard Schölkopf · 2020
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Variational autoencoders with riemannian brownian motion priors
Dimitrios Kalatzis, David Eklund, Georgios Arvanitidis, and Soren Hauberg · 2020
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On self-supervised image representations for gan evaluation
Stanislav Morozov, Andrey Voynov, and Artem Babenko · 2020
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Learning latent space energy-based prior model
Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, and Ying Nian Wu · 2020
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Generating diverse high-fidelity images with vq-vae-2
Ali Razavi, Aaron van den Oord, and Oriol Vinyals · 2020
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Pytorch-vae
A.K Subramanian · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
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Clément Chadebec, Elina Thibeau-Sutre, Ninon Burgos, and Stéphanie Allassonnière · 2021
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Variational autoencoder with learned latent structure
Marissa Connor, Gregory Canal, and Christopher Rozell · 2021
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Internalized biases in fréchet inception distance
Steffen Jung and Margret Keuper · 2021
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