# Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently

arXiv: https://arxiv.org/abs/1702.06762 · HTML: https://mlsanity.com/paper/1702.06762/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2017.

## Built on by (most cited first, 2 of 2)

- [Bayesian Transfer Learning: An Overview of Probabilistic Graphical Models for Transfer Learning](https://mlsanity.com/paper/2109.13233/) (2021)
- [See Blue Sky: Deep Image Dehaze Using Paired and Unpaired Training Images](https://mlsanity.com/paper/2210.07594/) (2022)

## Abstract (arXiv)

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.