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A longstanding problem in machine learning is to find unsupervised methods that can learn the statistical structure of high dimensional signals.
Biostatistics: a foundation for analysis in the health sciences
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Emergence of simple-cell receptive field properties by learning a sparse code for natural images
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Edges are the ’independent components’ of natural scenes
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Gradient-based learning applied to document recognition
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Mixtures of probabilistic principal component analyzers
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Michael E Tipping and Christopher M Bishop · 1999
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Alex Krizhevsky and Geoffrey Hinton · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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From learning models of natural image patches to whole image restoration
Daniel Zoran and Yair Weiss · 2011
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Rnade: The real-valued neural autoregressive density-estimator
Benigno Uria, Iain Murray, and Hugo Larochelle · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2014
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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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TensorFlow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
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InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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NIPS 2016 tutorial: Generative adversarial networks
Ian Goodfellow · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
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Deep feature consistent variational autoencoder
Xianxu Hou, Linlin Shen, Ke Sun, and Guoping Qiu · 2017
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros · 2017
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PixelCNN models with auxiliary variables for natural image modeling
Alexander Kolesnikov and Christoph H Lampert · 2017
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MMD GAN: Towards deeper understanding of moment matching network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabas Poczos · 2017
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Unrolled generative adversarial networks
Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 2017
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Conditional image synthesis with auxiliary classifier GANs
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2017
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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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Pixel recurrent neural networks
Aaron Van Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Wasserstein Generative Adversarial Networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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BEGAN: Boundary equilibrium generative adversarial networks
David Berthelot, Tom Schumm, and Luke Metz · 2017
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Comparison of maximum likelihood and GAN-based training of Real NVPs
Ivo Danihelka, Balaji Lakshminarayanan, Benigno Uria, Daan Wierstra, and Peter Dayan · 2017
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Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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VEEGAN: Reducing mode collapse in GANs using implicit variational learning
Akash Srivastava, Lazar Valkoz, Chris Russell, Michael U Gutmann, and Charles Sutton · 2017
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On the quantitative analysis of decoder-based generative models
Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, and Roger Grosse · 2017
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Do GANs learn the distribution? some theory and empirics
Sanjeev Arora, Andrej Risteski, and Yi Zhang · 2018
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Many paths to equilibrium: GANs do not need to decrease a divergence at every step
William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M Dai, Shakir Mohamed, and Ian Goodfellow · 2018
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Flow-GAN: Combining maximum likelihood and adversarial learning in generative models
Aditya Grover, Manik Dhar, and Stefano Ermon · 2018
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Quantitatively evaluating GANs with divergences proposed for training
Daniel Jiwoong Im, He Ma, Graham Taylor, and Kristin Branson · 2018
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Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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Are GANs created equal? a large-scale study
Mario Lučić, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2018
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