Unpaired Image-to-Image Translation via Neural Schrödinger Bridge

Beomsu Kim, Gihyun Kwon, Kwanyoung Kim, Jong Chul Ye

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Keywords: Style Transfer, Schrödinger Bridge Problem, Adversarial Training

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TL;DR: We apply Schrödinger Bridges to unpaired image-to-image translation.

Abstract: Diffusion models are a powerful class of generative models which simulate stochastic differential equations (SDEs) to generate data from noise. While diffusion models have achieved remarkable progress, they have limitations in unpaired image-to-image (I2I) translation tasks due to the Gaussian prior assumption. Schrödinger Bridge (SB), which learns an SDE to translate between two arbitrary distributions, have risen as an attractive solution to this problem. Yet, to our best knowledge, none of SB models so far have been successful at unpaired translation between high-resolution images. In this work, we propose Unpaired Neural Schrödinger Bridge (UNSB), which expresses the SB problem as a sequence of adversarial learning problems. This allows us to incorporate advanced discriminators and regularization to learn a SB between unpaired data. We show that UNSB is scalable and successfully solves various unpaired I2I translation tasks. Code: \url{https://github.com/cyclomon/UNSB}

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Primary Area: generative models

Submission Number: 4501