An Online Learning Approach to Generative Adversarial Networks

international conference on learning representations, 2018.

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We have presented a principled approach to training Generative Adversarial Networks, which is guaranteed to reach convergence to a mixed equilibrium for semi-shallow architectures

Abstract:

We consider the problem of training generative models with a Generative Adversarial Network (GAN). Although GANs can accurately model complex distributions, they are known to be difficult to train due to instabilities caused by a difficult minimax optimization problem. In this paper, we view the problem of training GANs as finding a mixed...More

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