  The goal of Image-to-image (I2I) translation is to transfer an image from a
source domain to a target domain, which has recently drawn increasing
attention. One major branch of this research is to formulate I2I translation
based on Generative Adversarial Network (GAN). As a zero-sum game, GAN can be
reformulated as a Partially-observed Markov Decision Process (POMDP) for
generators, where generators cannot access full state information of their
environments. This formulation illustrates the information insufficiency in the
GAN training. To mitigate this problem, we propose to add a communication
channel between discriminators and generators. We explore multiple architecture
designs to integrate the communication mechanism into the I2I translation
framework. To validate the performance of the proposed approach, we have
conducted extensive experiments on various benchmark datasets. The experimental
results confirm the superiority of our proposed method.
