  Image translation is a burgeoning field in computer vision where the goal is
to learn the mapping between an input image and an output image. However, most
recent methods require multiple generators for modeling different domain
mappings, which are inefficient and ineffective on some multi-domain image
translation tasks. In this paper, we propose a novel method, SingleGAN, to
perform multi-domain image-to-image translations with a single generator. We
introduce the domain code to explicitly control the different generative tasks
and integrate multiple optimization goals to ensure the translation.
Experimental results on several unpaired datasets show superior performance of
our model in translation between two domains. Besides, we explore variants of
SingleGAN for different tasks, including one-to-many domain translation,
many-to-many domain translation and one-to-one domain translation with
multimodality. The extended experiments show the universality and extensibility
of our model.
