Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently
This work addresses style transfer in non-image domains like chess, but it is incremental as it adapts existing GAN methods to a new application.
The authors tackled the problem of extending style transfer beyond image tasks by proposing a GAN-based formulation, applying it to learn chess-playing styles of specific players and providing empirical evidence for its viability.
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.