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Generative adversarial networks (GAN) approximate a target data distribution by jointly optimizing an objective function through a "two-player game" between a generator and a discriminator.
Functional Analysis
W. Rudin · 1991
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Principles of real analysis
C. D. Aliprantis and O. Burkinshaw · 1998
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Optimal transport, old and new
C. Villani · 2009
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Hilbert space embeddings and metrics on probability measures
B. K. Sriperumbudur, A. Gretton, K. Fukumizu, B. Schölkopf, and G. R. G. Lanckriet · 2010
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Training generative neural networks via maximum mean discrepancy optimization
G. K. Dziugaite, D. M. Roy, and Z. Ghahramani · 2015
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Generative moment matching networks
Y. Li, K. Swersky, and R. Zemel · 2015
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Banach Spaces of Continuous Functions as Dual Spaces
H. G. Dales, J. F.K. Dashiell, A.-M. Lau, and D. Strauss · 2016
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Stochastic optimization for large-scale optimal transport
A. Genevay, M. Cuturi, G. Peyré, and F. R. Bach · 2016
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f-GAN: Training generative neural samplers using variational divergence minimization
S. Nowozin, B. Cseke, and R. Tomioka · 2016
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M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Generalization and equilibrium in generative adversarial nets (gans)
S. Arora, R. Ge, Y. Liang, T. Ma, and Y. Zhang · 2017
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Improved training of wasserstein gans
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville · 2017
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Generative models and model criticism via optimized maximum mean discrepancy
D. J. Sutherland, H. F. Tung, H. Strathmann, S. De, A. Ramdas, A. J. Smola, and A. Gretton · 2017
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Lecture notes: Information-theoretic methods for high-dimensional statistics
Y. Wu · 2017
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