Unpaired Image-to-Image Translation via Neural Schr\"odinger Bridge

• URL: http://arxiv.org/abs/2305.15086v3
• Date: Sat, 2 Mar 2024 12:47:22 GMT
• Title: Unpaired Image-to-Image Translation via Neural Schr\"odinger Bridge
• Authors: Beomsu Kim, Gihyun Kwon, Kwanyoung Kim, Jong Chul Ye
• Abstract summary: We propose Unpaired Neural Schr"odinger Bridge (UNSB), which expresses the SB problem as a sequence of adversarial learning problems. UNSB is scalable and successfully solves various unpaired I2I translation tasks.
• Score: 70.79973551604539
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Schr\"{o}dinger 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 Schr\"{o}dinger 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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