Extremal Domain Translation with Neural Optimal Transport

In many unpaired image domain translation problems, e.g., style transfer or superresolution, it is important to keep the translated image similar to its respective input image. We propose the extremal transport (ET) which is a mathematical formalization of the theoretically best possible unpaired translation between a pair of domains w.r.t. the given similarity function. Inspired by the recent advances in neural optimal transport (OT), we propose a scalable algorithm to approximate ET maps as a limit of partial OT maps. We test our algorithm on toy examples and on the unpaired image-to-image translation task. The code is publicly available at https://github.com/milenagazdieva/ExtremalNeuralOptimalTransport (a) Handbag → shoes (128×128). (b) Celeba (female) → anime (64×64). Figure 1: (Nearly) extremal transport with our Algorithm 1. Higher w yields bigger similarity of x and T (x) in ℓ 2 .