Multimodal Image to Image Translation via Mutual Information Estimation and Maximization
In this paper, we present a novel framework that can achieve multimodal image to image translation by simply encouraging the statistical dependence between the latent code and the output image in conditional generative adversarial networks. In addition, by incorporating a U net generator into our framework, our method only needs to learn a one sided translation model from the source image domain to the target image domain for both supervised and unsupervised multimodal image to image translation. Furthermore, our method also achieves disentanglement between the source domain content and the target domain style for free. We conduct experiments under supervised and unsupervised settings on various benchmark image to image translation datasets compared with the state of the art methods, showing the effectiveness and simplicity of our method to achieve multimodal and high quality results.
ArXiv