Convergence of GANs Training: A Game and Stochastic Control Methodology

• URL: http://arxiv.org/abs/2112.00222v1
• Date: Wed, 1 Dec 2021 01:52:23 GMT
• Title: Convergence of GANs Training: A Game and Stochastic Control Methodology
• Authors: Othmane Mounjid, Xin Guo
• Abstract summary: Training of generative adversarial networks (GANs) is known for its difficulty to converge. This paper first confirms the lack of convexity in GANs objective functions, hence the well-posedness problem of GANs models. In particular, it presents an optimal solution for adaptive learning rate which depends on the convexity of the objective function.
• Score: 4.933916728941277
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training of generative adversarial networks (GANs) is known for its difficulty to converge. This paper first confirms analytically one of the culprits behind this convergence issue: the lack of convexity in GANs objective functions, hence the well-posedness problem of GANs models. Then, it proposes a stochastic control approach for hyper-parameters tuning in GANs training. In particular, it presents an optimal solution for adaptive learning rate which depends on the convexity of the objective function, and builds a precise relation between improper choices of learning rate and explosion in GANs training. Finally, empirical studies demonstrate that training algorithms incorporating this selection methodology outperform standard ones.

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