Extremal Domain Translation with Neural Optimal Transport

Milena Gazdieva; Alexander Korotin; Daniil Selikhanovych; Evgeny Burnaev

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arXiv · 2301.12874

Extremal Domain Translation with Neural Optimal Transport

Milena Gazdieva·Alexander Korotin·Daniil Selikhanovych·Evgeny Burnaev

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Abstract

In many unpaired image domain translation problems, e.g., style transfer or super-resolution, 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

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BibTeX RIS

Milena Gazdieva, Alexander Korotin, Daniil Selikhanovych, Evgeny Burnaev. 2023-11-02. Extremal Domain Translation with Neural Optimal Transport. https://arxiv.org/abs/2301.12874

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