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While Generative Adversarial Networks (GANs) have demonstrated promising performance on multiple vision tasks, their learning dynamics are not yet well understood, both in theory and in practice.
Zero Crossings and the Heat Equation
Hummel, R.A. and Gidas, B.C · 1984
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How (un) stable are Vandermonde systems?
Gautschi, Walter · 1990
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Combinatorial methods in density estimation
Devroye, Luc and Lugosi, Gábor · 2012
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Efficient density estimation via piecewise polynomial approximation
Chan, Siu-On, Diakonikolas, Ilias, Servedio, Rocco A, and Sun, Xiaorui · 2014
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Generative adversarial nets
Goodfellow, Ian, Pouget-Abadie, Jean, Mirza, Mehdi, Xu, Bing, Warde-Farley, David, Ozair, Sherjil, Courville, Aaron, and Bengio, Yoshua · 2014
Earlier work this paper cites.
Towards principled methods for training generative adversarial networks
Arjovsky, Martin and Bottou, Leon · 2017
Cited alongside, same era.
Wasserstein GAN
Arjovsky, Martin, Chintala, Soumith, and Bottou, Léon · 2017
Cited alongside, same era.
Generalization and equilibrium in generative adversarial nets (GANs)
Arora, Sanjeev, Ge, Rong, Liang, Yingyu, Ma, Tengyu, and Zhang, Yi · 2017
Cited alongside, same era.
NIPS 2016 tutorial: Generative adversarial networks
Goodfellow, Ian · 2017
Cited alongside, same era.
On functions deviating least from zero in a given interval
Markov, VA
Cited in the paper.
Unrolled generative adversarial networks
Metz, Luke, Poole, Ben, Pfau, David, and Sohl-Dickstein, Jascha · 2017
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Gradient descent gan optimization is locally stable
Nagarajan, Vaishnavh and Kolter, J. Zico · 2017
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Do gans actually learn the distribution? an empirical study
Arora, Sanjeev and Zhang, Yi · 2018
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An online learning approach to generative adversarial networks
Grnarova, Paulina, Levy, Kfir Y., Lucchi, Aurelien, Hofmann, Thomas, and Krause, Andreas · 2018
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