  State-of-the-art techniques in Generative Adversarial Networks (GANs) have
shown remarkable success in image-to-image translation from peer domain X to
domain Y using paired image data. However, obtaining abundant paired data is a
non-trivial and expensive process in the majority of applications. When there
is a need to translate images across n domains, if the training is performed
between every two domains, the complexity of the training will increase
quadratically. Moreover, training with data from two domains only at a time
cannot benefit from data of other domains, which prevents the extraction of
more useful features and hinders the progress of this research area. In this
work, we propose a general framework for unsupervised image-to-image
translation across multiple domains, which can translate images from domain X
to any a domain without requiring direct training between the two domains
involved in image translation. A byproduct of the framework is the reduction of
computing time and computing resources, since it needs less time than training
the domains in pairs as is done in state-of-the-art works. Our proposed
framework consists of a pair of encoders along with a pair of GANs which learns
high-level features across different domains to generate diverse and realistic
samples from. Our framework shows competing results on many image-to-image
tasks compared with state-of-the-art techniques.

Computer Vision and Pattern Recognition;Image and Video Processing