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 representation function f, which accepts inputs in either domains, would remain unchanged. Other than f, the training data is unsupervised and consist of a set of samples from each domain, without any mapping between them. The Domain Transfer Network (DTN) we present employs a compound loss function that includes a multiclass GAN loss, an f preserving 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.
|State||Published - 2017|
|Event||5th International Conference on Learning Representations, ICLR 2017 - Toulon, France|
Duration: 24 Apr 2017 → 26 Apr 2017
|Conference||5th International Conference on Learning Representations, ICLR 2017|
|Period||24/04/17 → 26/04/17|