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We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.
Iterative solution of games by fictitious play
George W Brown · 1951
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Measuring statistical dependence with hilbert-schmidt norms
Arthur Gretton, Olivier Bousquet, Alex Smola, and Bernhard Schölkopf · 2005
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Kernel measures of conditional dependence
Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, and Bernhard Schölkopf · 2007
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A hilbert space embedding for distributions
Alex Smola, Arthur Gretton, Le Song, and Bernhard Schölkopf · 2007
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, et al · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, et al · 2014
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On distinguishability criteria for estimating generative models
Ian J Goodfellow · 2014
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Semi-supervised learning with deep generative models
Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
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Deep generative image models using a laplacian pyramid of adversarial networks
Emily Denton, Soumith Chintala, Arthur Szlam, and Rob Fergus · 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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Generative moment matching networks
Yujia Li, Kevin Swersky, and Richard S. 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
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
Semi-supervised learning with ladder networks
Antti Rasmus, Mathias Berglund, Mikko Honkala, Harri Valpola, and Tapani Raiko · 2015
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, et al · 2015
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Generating images with recurrent adversarial networks
Daniel Jiwoong Im, Chris Dongjoo Kim, Hui Jiang, and Roland Memisevic · 2016
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Donggeun Yoo, Namil Kim, Sunggyun Park, Anthony S Paek, and In So Kweon · 2016
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Adversarial perturbations of deep neural networks
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Ilya Sutskever, Rafal Jozefowicz, Karol Gregor, et al · 2015
Cited alongside, same era.
Unsupervised and semi-supervised learning with categorical generative adversarial networks
Jost Tobias Springenberg · 2015
Cited alongside, same era.
Rethinking the Inception Architecture for Computer Vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2015
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2015
Cited alongside, same era.
Distributional smoothing by virtual adversarial examples
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, and Shin Ishii · 2015
Cited alongside, same era.
David Warde-Farley and Ian Goodfellow · 2016
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Diederik P Kingma · 2016
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Auxiliary deep generative models
Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, and Ole Winther · 2016
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