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Generative Adversarial Networks (GANs) are powerful models for learning complex distributions.
Infinite-dimensional Optimization and Convexity
I. Ekeland and T. Turnbull · 1983
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Integral probability metrics and their generating classes of functions
Alfred Müller · 1997
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Local rademacher complexities
Peter L. Bartlett, Olivier Bousquet, and Shahar Mendelson · 2005
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Numerical Optimization
J. Nocedal and S. J. Wright · 2006
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Testing for homogeneity with kernel fisher discriminant analysis
Zaïd Harchaoui, Francis R Bach, and Eric Moulines · 2008
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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On integral probability metrics, ϕ \phi -divergences and binary classification
Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Scholkopf, and Gert R. G. Lanckriet · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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On the empirical estimation of integral probability metrics
Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Schölkopf, and Gert R. G. Lanckriet · 2012
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 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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Metrics for probabilistic geometries
Alessandra Tosi, Søren Hauberg, Alfredo Vellido, and Neil D. Lawrence · 2014
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Generative moment matching networks
Yujia Li, Kevin Swersky, and Richard Zemel · 2015
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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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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 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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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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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Stacked generative adversarial networks
Xun Huang, Yixuan Li, Omid Poursaeed, John Hopcroft, and Serge Belongie · 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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Conditional image synthesis with auxiliary classifier gans
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2016
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Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou · 2017
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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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Unsupervised and semi-supervised learning with categorical generative adversarial networks
Jost Tobias Springenberg · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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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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Least squares generative adversarial networks
Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, and Zhen Wang · 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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Learning in implicit generative models
Shakir Mohamed and Balaji Lakshminarayanan · 2016
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Amortised map inference for image super-resolution
Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, and Ferenc Huszár · 2017
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Wasserstein gan
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Improved training of wasserstein gans
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Mcgan: Mean and covariance feature matching gan
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