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The Generative Adversarial Networks (GAN) framework is a well-established paradigm for probability matching and realistic sample generation.
A relationship between arbitrary positive matrices and doubly stochastic matrices
R. Sinkhorn · 1964
Earlier work this paper cites.
Maximum likelihood from incomplete data via the em algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
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Pattern recognition and machine learning
Christopher M Bishop · 2006
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A fast learning algorithm for deep belief nets
Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh · 2006
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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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Least-squares conditional density estimation
Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, and Daisuke Okanohara · 2010
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A user’s guide to optimal transport
Luigi Ambrosio and Nicola Gigli · 2013
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Perturbation analysis of optimization problems
J Frédéric Bonnans and Alexander Shapiro · 2013
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Inverse problems in spaces of measures
Kristian Bredies and Hanna Katriina Pikkarainen · 2013
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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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
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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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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Stochastic optimization for large-scale optimal transport
Aude Genevay, Marco Cuturi, Gabriel Peyré, and Francis Bach · 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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Conditional image generation with pixelcnn decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
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Pixel recurrent neural networks
Aaron Van Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Generating videos with scene dynamics
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Generalization and equilibrium in generative adversarial nets (gans)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017
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The alternating descent conditional gradient method for sparse inverse problems
Nicholas Boyd, Geoffrey Schiebinger, and Benjamin Recht · 2017
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
Finding mixed nash equilibria of generative adversarial networks
Ya-Ping Hsieh, Chen Liu, and Volkan Cevher · 2018
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On how well generative adversarial networks learn densities: Nonparametric and parametric results
Tengyuan Liang · 2018
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Sliced-wasserstein flows: Nonparametric generative modeling via optimal transport and diffusions
Antoine Liutkus, Umut Şimşekli, Szymon Majewski, Alain Durmus, and Fabian-Robert Stöter · 2018
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On the convergence and robustness of training gans with regularized optimal transport
Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn, and Jason D Lee · 2018
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Nonparametric density estimation with adversarial losses
Shashank Singh, Ananya Uppal, Boyue Li, Chun-Liang Li, Manzil Zaheer, and Barnabás Póczos · 2018
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
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Approximation and convergence properties of generative adversarial learning
Shuang Liu, Olivier Bousquet, and Kamalika Chaudhuri · 2017
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Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, and Yu Cheng · 2017
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Fisher gan
Youssef Mroueh and Tom Sercu · 2017
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Density estimation in infinite dimensional exponential families
Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, and Revant Kumar · 2017
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Approximability of discriminators implies diversity in gans
Yu Bai, Tengyu Ma, and Andrej Risteski · 2018
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On the discrimination-generalization tradeoff in GANs
Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, and Xiaodong He · 2018
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Sparse optimization on measures with over-parameterized gradient descent
Lenaic Chizat · 2019
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Interpolating between optimal transport and mmd using sinkhorn divergences
Jean Feydy, Thibault Séjourné, François-Xavier Vialard, Shun-Ichi Amari, Alain Trouvé, and Gabriel Peyré · 2019
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Sinkhorn barycenters with free support via frank-wolfe algorithm
Giulia Luise, Saverio Salzo, Massimiliano Pontil, and Carlo Ciliberto · 2019
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Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
Gonzalo Mena and Jonathan Niles-Weed · 2019
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Geometric losses for distributional learning
Arthur Mensch, Mathieu Blondel, and Gabriel Peyré · 2019
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Asymptotic guarantees for learning generative models with the sliced-wasserstein distance
Kimia Nadjahi, Alain Durmus, Umut Simsekli, and Roland Badeau · 2019
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Mmgan: Generative adversarial networks for multi-modal distributions
Teodora Pandeva and Matthias Schubert · 2019
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Computational optimal transport
Gabriel Peyré and Marco Cuturi · 2019
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Nonparametric density estimation & convergence rates for gans under besov ipm losses
Ananya Uppal, Shashank Singh, and Barnabas Poczos · 2019
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Sharp asymptotic and finite-sample rates of convergence of empirical measures in wasserstein distance
Jonathan Weed, Francis Bach, et al · 2019
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Sliced wasserstein generative models
Jiqing Wu, Zhiwu Huang, Dinesh Acharya, Wen Li, Janine Thoma, Danda Pani Paudel, and Luc Van Gool · 2019
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