Unsupervised Image-to-Image Translation Networks

Most of the existing image-to-image translation frameworks---mapping an image in one domain to a corresponding image in another---are based on supervised learning, i.e., pairs of corresponding images in two domains are required for learning the translation function. This largely limits their applications, because capturing corresponding images in two different domains is often a difficult task. To address the issue, we propose the UNsupervised Image-to-image Translation (UNIT) framework. The proposed framework is based on variational autoencoders and generative adversarial networks. It can learn the translation function without any corresponding images. We show this learning capability is enabled by combining a weight-sharing constraint and an adversarial objective and verify the effectiveness of the proposed framework through extensive experiment results.