Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently
Muthuraman Chidambaram, Yanjun Qi
Feb 16, 2017 (modified: Mar 16, 2017)ICLR 2017 workshop submissionreaders: everyone
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.
Enter your feedback below and we'll get back to you as soon as possible.