  Current approaches have made great progress on image-to-image translation
tasks benefiting from the success of image synthesis methods especially
generative adversarial networks (GANs). However, existing methods are limited
to handling translation tasks between two species while keeping the content
matching on the semantic level. A more challenging task would be the
translation among more than two species. To explore this new area, we propose a
simple yet effective structure of a multi-branch discriminator for enhancing an
arbitrary generative adversarial architecture (GAN), named GAN-MBD. It takes
advantage of the boosting strategy to break a common discriminator into several
smaller ones with fewer parameters, which can enhance the generation and
synthesis abilities of GANs efficiently and effectively. Comprehensive
experiments show that the proposed multi-branch discriminator can dramatically
improve the performance of popular GANs on cross-species image-to-image
translation tasks while reducing the number of parameters for computation. The
code and some datasets are attached as supplementary materials for reference.
