  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. A generative model with an XO-structure, called the
XOGAN, is proposed to learn the cross domain relationship among the two domains
and the ad- ditional variables. Not only can we learn to translate between the
two image domains, we can also handle the translated images with additional
variations. Experiments are performed on unpaired image generation tasks,
including edges-to-objects translation and facial image translation. We show
that the proposed XOGAN model can generate plausible images and control
variations, such as color and texture, of the generated images. Moreover, while
state-of-the-art unpaired image generation algorithms tend to generate images
with monotonous colors, XOGAN can generate more diverse results.
