Image Style Transfer with Generative Adversarial Networks

Abstract

Image style transfer is a recently popular research field, which aims to learn the mapping between different domains and involves different computer vision techniques. Recently, Generative Adversarial Networks (GAN) have demonstrated their potentials of translating images from source domain X to target domain Y in the absence of paired examples. However, such a translation cannot guarantee to generate high perceptual quality results. Existing style transfer methods work well with relatively uniform content, they often fail to capture geometric or structural patterns that reflect the quality of generated images. The goal of this doctoral research is to investigate the image style transfer approaches, and design advanced and useful methods to solve existing problems. Though preliminary experiments conducted so far, we demonstrate our insights on the image style translation approaches, and present the directions to be pursued in the future.