  We propose coupled generative adversarial network (CoGAN) for learning a
joint distribution of multi-domain images. In contrast to the existing
approaches, which require tuples of corresponding images in different domains
in the training set, CoGAN can learn a joint distribution without any tuple of
corresponding images. It can learn a joint distribution with just samples drawn
from the marginal distributions. This is achieved by enforcing a weight-sharing
constraint that limits the network capacity and favors a joint distribution
solution over a product of marginal distributions one. We apply CoGAN to
several joint distribution learning tasks, including learning a joint
distribution of color and depth images, and learning a joint distribution of
face images with different attributes. For each task it successfully learns the
joint distribution without any tuple of corresponding images. We also
demonstrate its applications to domain adaptation and image transformation.
