TiedGAN: Multi-domain Image Transformation Networks

Mohammad Ahangar Kiasari, Dennis Singh Moirangthem, Jonghong Kim, Minho Lee

Abstract: Recently, domain transformation has become a popular challenge in deep generative networks. One of the recent well-known domain transformation model named CycleGAN, has shown good performance in transformation task from one domain to another domain. However, CycleGAN lacks the capability to address multi-domain transformation problems because of its high complexity. In this paper, we propose TiedGAN in order to achieve multi-domain image transformation with reduced complexity. The results of our experiment indicate that the proposed model has comparable performance to CycleGAN as well as successfully alleviates the complexity issue in the multi-domain transformation task.