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Generative adversarial network (GAN) is a minimax game between a generator mimicking the true model and a discriminator distinguishing the samples produced by the generator from the real training samples.
The mnist database of handwritten digits
Yann LeCun · 1998
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Information theory and statistics: A tutorial
Imre Csiszár, Paul C Shields, et al · 2004
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Convex optimization
Stephen Boyd and Lieven Vandenberghe · 2004
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Unifying divergence minimization and statistical inference via convex duality
Yasemin Altun and Alex Smola · 2006
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Maximum entropy density estimation with generalized regularization and an application to species distribution modeling
Miroslav Dudík, Steven J Phillips, and Robert E Schapire · 2007
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Optimal transport: old and new
Cédric Villani · 2008
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Convergence of probability measures
Patrick Billingsley · 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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Training generative neural networks via maximum mean discrepancy optimization
Gintare Karolina Dziugaite, Daniel M Roy, and Zoubin Ghahramani · 2015
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Generative moment matching networks
Yujia Li, Kevin Swersky, and Rich Zemel · 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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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Discrete rényi classifiers
Meisam Razaviyayn, Farzan Farnia, and David Tse · 2015
Cited alongside, same era.
Nips 2016 tutorial: Generative adversarial networks
Ian Goodfellow · 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.
Energy-based generative adversarial network
Junbo Zhao, Michael Mathieu, and Yann LeCun · 2016
Cited alongside, same era.
A minimax approach to supervised learning
Farzan Farnia and David Tse · 2016
Cited alongside, same era.
Adversarial multiclass classification: A risk minimization perspective
Do gans actually learn the distribution? an empirical study
Sanjeev Arora and Yi Zhang · 2017
Later among the works it cites.
A classification-based perspective on gan distributions
Shibani Santurkar, Ludwig Schmidt, and Aleksander Madry · 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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Gradient descent gan optimization is locally stable
Vaishnavh Nagarajan and J Zico Kolter · 2017
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The numerics of gans
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
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Rizal Fathony, Anqi Liu, Kaiser Asif, and Brian Ziebart · 2016
Cited alongside, same era.
A very complicated proof of the minimax theorem
Jonathan M Borwein · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Mmd gan: Towards deeper understanding of moment matching network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabás Póczos · 2017
Cited alongside, same era.
Understanding gans: the lqg setting
Soheil Feizi, Farzan Farnia, Tony Ginart, and David Tse · 2017
Cited alongside, same era.
Generalization and equilibrium in generative adversarial nets (gans)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017
Cited alongside, same era.
Approximation and convergence properties of generative adversarial learning
Shuang Liu, Olivier Bousquet, and Kamalika Chaudhuri · 2017
Cited alongside, same era.
Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, and Haoyang Zeng · 2017
Later among the works it cites.
Adversarial surrogate losses for ordinal regression
Rizal Fathony, Mohammad Ali Bashiri, and Brian Ziebart · 2017
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Closest in time.
Certifiable distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2018
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On the discrimination-generalization tradeoff in GANs
Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, and Xiaodong He · 2018
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Training generative adversarial networks via primal-dual subgradient methods: a Lagrangian perspective on GAN
Xu Chen, Jiang Wang, and Hao Ge · 2018
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The information autoencoding family: A lagrangian perspective on latent variable generative models
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2018
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Solving approximate wasserstein gans to stationarity
Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn, and Jason D Lee · 2018
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Yusuke Tsuzuku, Issei Sato, and Masashi Sugiyama · 2018
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