iFlowGAN: An Invertible Flow-based Generative Adversarial Network For Unsupervised Image-to-Image Translation.

We propose iFlowGAN that learns an invertible flow (a sequence of invertible mappings) via adversarial learning and exploit it to transform a source distribution into a target distribution for unsupervised image-to-image translation. Inspired by zero-order reverse filtering, we 1, understand the forward mapping F via contraction mappings on a metric space; 2, provide a simple yet effective algorithm to present the backwad mapping B via the parameters of F in light of Banach fixed point theorem; 3, provide a Lipschitz-regularized network which indicates a general approach to compose the inverse for arbitrary Lipschitz-regularized networks via Banach fixed point theorem. Taking advantage of the Lipschitz-regularized network, we not only build iFlowGAN to solve the redundancy shortcoming of CycleGAN but also assemble the corresponding iFlowGAN versions of StarGAN, AGGAN and CyCADA without breaking their network architectures. Extensive experiments show that the iFlowGAN version could produce comparable results of the original implementation while saving half parameters.