  In this paper, we study the problem of multi-domain image generation, the
goal of which is to generate pairs of corresponding images from different
domains. With the recent development in generative models, image generation has
achieved great progress and has been applied to various computer vision tasks.
However, multi-domain image generation may not achieve the desired performance
due to the difficulty of learning the correspondence of different domain
images, especially when the information of paired samples is not given. To
tackle this problem, we propose Regularized Conditional GAN (RegCGAN) which is
capable of learning to generate corresponding images in the absence of paired
training data. RegCGAN is based on the conditional GAN, and we introduce two
regularizers to guide the model to learn the corresponding semantics of
different domains. We evaluate the proposed model on several tasks for which
paired training data is not given, including the generation of edges and
photos, the generation of faces with different attributes, etc. The
experimental results show that our model can successfully generate
corresponding images for all these tasks, while outperforms the baseline
methods. We also introduce an approach of applying RegCGAN to unsupervised
domain adaptation.
