Unsupervised multi-domain image translation with domain representation learning

Recent years have witnessed tremendous improvements in multi-domain image-to-image translation. However, previous methods require multi-generator models or one-generator model with labeled datasets, which will increase the training cost and limit their further applications. In this paper, we propose an unsupervised network that consists of one pair of generator and discriminator as well as n encoders to achieve multi-domain image translation. Our work aims to learn the mappings of n domains simultaneously and automatically using images without any attribute labels. Besides, a representation loss is proposed to extract a proper representation vector for each domain, which would improve the performance of multi-domain image translation. Experiments have shown that our proposed method can perform better than the state-of-the-art multi-domain methods.