Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently

Muthuraman Chidambaram, Yanjun Qi

Abstract: The idea of style transfer has largely only been explored in image-based tasks, which we attribute in part to the specific nature of loss functions used for style transfer. We propose a general formulation of style transfer as an extension of generative adversarial networks, by using a discriminator to regularize a generator with an otherwise separate loss function. We apply our approach to the task of learning to play chess in the style of a specific player, and present empirical evidence for the viability of our approach.

TL;DR: A discriminator network is used to regularize a separate generator network to influence the style with which the generator performs a task.

Keywords: Deep learning

Conflicts: virginia.edu