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We propose a new technique that boosts the convergence of training generative adversarial networks.
Optimization by vector space methods
David G Luenberger · 1969
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Geometric problems in the theory of infinite-dimensional probability distributions
Vladimir N Sudakov · 1979
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Décomposition polaire et réarrangement monotone des champs de vecteurs
Yann Brenier · 1987
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Polar factorization and monotone rearrangement of vector-valued functions
Yann Brenier · 1991
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The geometry of dissipative evolution equations: the porous medium equation
Felix Otto · 2001
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Envelope theorems for arbitrary choice sets
Paul Milgrom and Ilya Segal · 2002
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Lecture notes on optimal transport problems
Luigi Ambrosio · 2003
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Gradient Flows in Metric Spaces and in the Space of Probability Measures
Luigi Ambrosio, Nicola Gigli, and Giuseppe Savaré · 2008
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Optimal transport: old and new
Cédric Villani · 2008
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton · 2012
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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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Deep residual learning for image recognition
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 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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Theano: A Python framework for fast computation of mathematical expressions
Theano Development Team · 2016
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Learning to draw samples: With application to amortized mle for generative adversarial learning
Dilin Wang and Qiang Liu · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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f-GAN: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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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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Naveen Kodali, Jacob Abernethy, James Hays, and Zsolt Kira · 2017
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StackGAN: Text to photo-realistic image synthesis with stacked generative adversarial networks
Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris N Metaxas · 2017
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