【论文】（论文+代码）无监督的跨域图像生成

We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given function f, which accepts inputs in either domains, would remain unchanged. Other than the function f, the training data is unsupervised and consist of a set of samples from each domain. The Domain Transfer Network (DTN) we present employs a compound loss function that includes a multiclass GAN loss, an f-constancy component, and a regularizing component that encourages G to map samples from T to themselves. We apply our method to visual domains including digits and face images and demonstrate its ability to generate convincing novel images of previously unseen entities, while preserving their identity.

论文链接：

https://arxiv.org/abs/1611.02200

代码链接：

https://github.com/yunjey/dtn-tensorflow

原文链接：

http://weibo.com/5501429448/Es9L6oxfG?ref=collection&type=comment#_rnd1485161563006