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.
@article{arxiv.1702.06762,
title = {Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently},
author = {Muthuraman Chidambaram and Yanjun Qi},
journal= {arXiv preprint arXiv:1702.06762},
year = {2017}
}