Solving a class of non-convex min-max games using adaptive momentum methods

• URL: http://arxiv.org/abs/2104.12676v1
• Date: Mon, 26 Apr 2021 16:06:39 GMT
• Title: Solving a class of non-convex min-max games using adaptive momentum methods
• Authors: Babak Barazandeh, Davoud Ataee Tarzanagh, George Michailidis
• Abstract summary: Adaptive momentum methods have attracted a lot of attention for deep neural networks. In this paper, we propose an adaptive momentum min-max optimization problem in adversarial networks. Experimental results illustrate its superior performance vis-avis methods for solving such problems.
• Score: 9.538456363995161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adaptive momentum methods have recently attracted a lot of attention for training of deep neural networks. They use an exponential moving average of past gradients of the objective function to update both search directions and learning rates. However, these methods are not suited for solving min-max optimization problems that arise in training generative adversarial networks. In this paper, we propose an adaptive momentum min-max algorithm that generalizes adaptive momentum methods to the non-convex min-max regime. Further, we establish non-asymptotic rates of convergence for the proposed algorithm when used in a reasonably broad class of non-convex min-max optimization problems. Experimental results illustrate its superior performance vis-a-vis benchmark methods for solving such problems.

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