  In this paper, we aim at solving the multi-domain image-to-image translation
problem with a unified model in an unsupervised manner. The most successful
work in this area refers to StarGAN, which works well in tasks like face
attribute modulation. However, StarGAN is unable to match multiple translation
mappings when encountering general translations with very diverse domain
shifts. On the other hand, StarGAN adopts an Encoder-Decoder-Discriminator
(EDD) architecture, where the model is time-consuming and unstable to train. To
this end, we propose a Compact, effective, robust, and fast GAN model, termed
CerfGAN, to solve the above problem. In principle, CerfGAN contains a novel
component, i.e., a multi-class discriminator (MCD), which gives the model an
extremely powerful ability to match multiple translation mappings. To stabilize
the training process, MCD also plays a role of the encoder in CerfGAN, which
saves a lot of computation and memory costs. We perform extensive experiments
to verify the effectiveness of the proposed method. Quantitatively, CerfGAN is
demonstrated to handle a serial of image-to-image translation tasks including
style transfer, season transfer, face hallucination, etc, where the input
images are sampled from diverse domains. The comparisons to several recently
proposed approaches demonstrate the superiority and novelty of the proposed
method.
