  Unsupervised multi-domain image-to-image translation aims to synthesis images
among multiple domains without labeled data, which is more general and
complicated than one-to-one image mapping. However, existing methods mainly
focus on reducing the large costs of modeling and do not pay enough attention
to the quality of generated images. In some target domains, their translation
results may not be expected or even it has model collapse. To improve the image
quality, we propose an effective many-to-many mapping framework for
unsupervised multi-domain image-to-image translation. There are two key aspects
in our method. The first is a proposed many-to-many architecture with only one
domain-shared encoder and several domain-specialized decoders to effectively
and simultaneously translate images across multiple domains. The second is two
proposed constraints extended from one-to-one mappings to further help improve
the generation. All the evaluations demonstrate our framework is superior to
existing methods and provides an effective solution for multi-domain
image-to-image translation.
