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Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has been made in understanding and improving GAN performance in terms of both theory and application.
Integral probability metrics and their generating classes of functions
Alfred Muller · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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A fast learning algorithm for deep belief nets
Geoffrey E. Hinton, Simon Osindero, and Yee Whye Teh · 2006
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Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization
XuanLong Nguyen, Martin J. Wainwright, and Michael I. Jordan · 2008
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Deep boltzmann machines
Ruslan Salakhutdinov and Geoffrey E. Hinton · 2009
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On integral probability metrics, phi-divergences and binary classification
Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, and Gert Lanckriet · 2009
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Grundlehren der mathematischen wissenschaften
Cedric Villani · 2009
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A kernel two-sample test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Scholkopf, and Alexander Smola · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Nice: non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Generative adversarial nets
Ian J. 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 varational bayes
Diederik P Kingma and Max Welling · 2014
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Neural variational inference and learning in belief networks
Andriy Mnih and Karol Gregor · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Draw: A recurrent neural network for image generation
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, and Daan Wierstra · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Denoising criterion for variational auto-encoding framework
Daniel Jiwoong Im, Sungjin Ahn, Roland Memisevic, and Yoshua Bengio · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Cited alongside, same era.
The cramer distance as a solution to biased wasserstein gradients
Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, and Rémi Munos · 2017
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Began: Boundary equilibrium generative adversarial networks
David Berthelot, Thomas Schumm, and Luke Metz · 2017
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Mode regularized generative adversarial networks
Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, and Wenjie Li · 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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Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2015
Cited alongside, same era.
A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2015
Cited alongside, same era.
Nips 2016 tutorial: Generative adversarial networks
Ian Goodfellow · 2016
Cited alongside, same era.
Energy-based generative adversarial network
Yann LeCun Junbo Zhao, Michael Mathieu · 2016
Cited alongside, same era.
Revisiting classifier two-sample tests
David Lopez-Paz and Maxime Oquab · 2016
Cited alongside, same era.
f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
Cited alongside, same era.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Cited alongside, same era.
On the quantitative analysis of decoder based generative models
Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, and Roger Grosse · 2016
Cited alongside, same era.
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 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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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
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On convergence and stability of gans
Naveen Kodali, Jacob Abernethy, James Hays, and Zsolt Kira · 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 Barnabás Póczos · 2017
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Least squares generative adversarial networks
Xudong Mao, Qing Li, Haoran Xie, Raymond Y.K. Lau, Zhen Wang, and Stephen Paul Smolley · 2017
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The numerics of gans
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
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Generative models and model criticism via optimized maximum mean discrepancy
Dougal J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De Aaditya Ramdas, Alex Smola, and Arthur Gretton · 2017
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https://github.com/znxlwm/pytorch-generative-model-collections
pytorch-generative-model-collections · 2018
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