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Generative adversarial networks (GANs) represent a zero-sum game between two machine players, a generator and a discriminator, designed to learn the distribution of data.
Nonlinear programming
Dimitri P Bertsekas · 1997
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The mnist database of handwritten digits
Yann LeCun · 1998
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Convex optimization
Stephen Boyd and Lieven Vandenberghe · 2004
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Optimal transport: old and new
Cédric Villani · 2008
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Neural networks for machine learning lecture 6a overview of mini-batch gradient descent
Geoffrey Hinton, Nitish Srivastava, and Kevin Swersky · 2012
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A user’s guide to optimal transport
Luigi Ambrosio and Nicola Gigli · 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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 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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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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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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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Nips 2016 tutorial: Generative adversarial networks
Ian Goodfellow · 2016
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Generalization and equilibrium in generative adversarial nets (gans)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017
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Understanding gans: the lqg setting
Soheil Feizi, Farzan Farnia, Tony Ginart, and David Tse · 2017
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Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, and Haoyang Zeng · 2017
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Gradient descent gan optimization is locally stable
Vaishnavh Nagarajan and J Zico Kolter · 2017
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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 · 2017
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Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, and Yu Cheng · 2017
A convex duality framework for gans
Farzan Farnia and David Tse · 2018
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Improving gans using optimal transport
Tim Salimans, Han Zhang, Alec Radford, and Dimitris Metaxas · 2018
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On the convergence and robustness of training gans with regularized optimal transport
Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn, and Jason D Lee · 2018
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Minmax optimization: Stable limit points of gradient descent ascent are locally optimal
Chi Jin, Praneeth Netrapalli, and Michael I Jordan · 2019
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Solving a class of non-convex min-max games using iterative first order methods
Maher Nouiehed, Maziar Sanjabi, Tianjian Huang, Jason D Lee, and Meisam Razaviyayn · 2019
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Began: Boundary equilibrium generative adversarial networks
David Berthelot, Thomas Schumm, and Luke Metz · 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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The numerics of gans
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
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Stabilizing training of generative adversarial networks through regularization
Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, and Thomas Hofmann · 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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Approximation and convergence properties of generative adversarial learning
Shuang Liu, Olivier Bousquet, and Kamalika Chaudhuri · 2017
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Aryan Mokhtari, Asuman Ozdaglar, and Sarath Pattathil · 2019
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Efficient algorithms for smooth minimax optimization
Kiran K Thekumparampil, Prateek Jain, Praneeth Netrapalli, and Sewoong Oh · 2019
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Policy optimization provably converges to nash equilibria in zero-sum linear quadratic games
Kaiqing Zhang, Zhuoran Yang, and Tamer Basar · 2019
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On finding local nash equilibria (and only local nash equilibria) in zero-sum games
Eric V Mazumdar, Michael I Jordan, and S Shankar Sastry · 2019
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Convergence of learning dynamics in stackelberg games
Tanner Fiez, Benjamin Chasnov, and Lillian J Ratliff · 2019
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On the convergence and robustness of adversarial training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, and Quanquan Gu · 2019
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On gradient descent ascent for nonconvex-concave minimax problems
Tianyi Lin, Chi Jin, and Michael I Jordan · 2019
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Sgd learns one-layer networks in wgans
Qi Lei, Jason D Lee, Alexandros G Dimakis, and Constantinos Daskalakis · 2019
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Lipschitz generative adversarial nets
Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, and Zhihua Zhang · 2019
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Amirhossein Taghvaei and Amin Jalali · 2019
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Generalizable adversarial training via spectral normalization
Farzan Farnia, Jesse Zhang, and David Tse · 2019
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On solving minimax optimization locally: A follow-the-ridge approach
Yuanhao Wang, Guodong Zhang, and Jimmy Ba · 2020
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