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DO-GAN: A Double Oracle Framework for Generative Adversarial Networks. (arXiv:2102.08577v2 [cs.LG] UPDATED)
April 27, 2022, 1:12 a.m. | Aye Phyu Phyu Aung, Xinrun Wang, Runsheng Yu, Bo An, Senthilnath Jayavelu, Xiaoli Li
cs.LG updates on arXiv.org arxiv.org
In this paper, we propose a new approach to train Generative Adversarial
Networks (GANs) where we deploy a double-oracle framework using the generator
and discriminator oracles. GAN is essentially a two-player zero-sum game
between the generator and the discriminator. Training GANs is challenging as a
pure Nash equilibrium may not exist and even finding the mixed Nash equilibrium
is difficult as GANs have a large-scale strategy space. In DO-GAN, we extend
the double oracle framework to GANs. We first generalize …
arxiv framework gan generative adversarial networks networks oracle
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