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Generative Adversarial Networks (GAN) have become one of the most successful frameworks for unsupervised generative modeling.
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Towards Principled Methods for Training Generative Adversarial Networks. In Proc. of the International Conference on Learning Representations (ICLR)
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Wasserstein Generative Adversarial Networks. In Proc. of the International Conference on Machine Learning (ICML)
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Generalization and Equilibrium in Generative Adversarial Nets (GANs). In Proc. of the International Conference on Machine Learning (ICML)
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Do GANs actually learn the distribution? An empirical study
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How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
A. Radford, L. Metz, and S. Chintala. 2015 · 2015
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Yang Cai, Ozan Candogan, Constantinos Daskalakis, and Christos H. Papadimitriou. 2016 · 2016
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Soumith Chintala, Aaron Courville, Emily Denton, Ian Goodfellow, Arthur Gretton, Yann LeCun, and Sebastian Nowozin. 2016 · 2016
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Dylan J Foster, Zhiyuan Li, Thodoris Lykouris, Karthik Sridharan, and Eva Tardos. 2016 · 2016
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D. Jiwoong Im, H. Ma, C. Dongjoo Kim, and G. Taylor. 2016 · 2016
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Wei Li, Melvin Gauci, and Roderich Groß. 2016 · 2016
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Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard H. Hovy, and Aaron C. Courville. 2017 · 2017
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Training GANs with Optimism
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An Online Learning Approach to Generative Adversarial Networks
P. Grnarova, K. Y. Levy, A. Lucchi, T. Hofmann, and A. Krause. 2017 · 2017
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Improved Training of Wasserstein GANs
Ishaan Gulrajani, Faruk Ahmed, Martín Arjovsky, Vincent Dumoulin, and Aaron C. Courville. 2017 · 2017
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Efficient Regret Minimization in Non-Convex Games. In International Conference on Machine Learning (ICML)
Elad Hazan, Karan Singh, and Cyril Zhang. 2017 · 2017
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GAN Implementations
Yong-Siang Shih. 2017 · 2017
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Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
T. Unterthiner, B. Nessler, G. Klambauer, M. Heusel, H. Ramsauer, and S. Hochreiter. 2017 · 2017
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Energy-based Generative Adversarial Network. In Proc. of the International Conference on Learning Representations (ICLR)
Junbo Jake Zhao, Michaël Mathieu, and Yann LeCun. 2017 · 2017
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