XOGAN: One-to-Many Unsupervised Image-to-Image Translation

Understand

Unsupervised image-to-image translation aims at learning the relationship between samples from two image domains without supervised pair information.

• The relationship between two domain images can be one-to-one, one-to-many or many-to-many.
• In this paper, we study the one-to-many unsupervised image translation problem in which an input sample from one domain can correspond to multiple samples in the other domain.
• To learn the complex relationship between the two domains, we introduce an additional variable to control the variations in our one-to-many mapping.