A survey on style transfer using generative adversarial networks
Zixuan Wang, Yanshan Wu · 2022
Developing and investigating recently, a new type of model appeared to greatly help the human with generating models. It can be trained with both supervised or unsupervised learning and contain both generative and discriminative models. Style transfer is one of the functions the GAN can be trained to produce, that it can synthesis two images together to get a new result by having one of the pictures as subject and the other one as style. In this paper, the work will introduce GAN and style transfer in detail and the application of style transfer in the real world.