Unsupervised Image-to-Image Translation Networks


Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains. Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions. To address the problem the authors make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs.


Want to receive more content like this in your inbox?