Diffusion models are a powerful class of generative models which simulate stochastic differential equations (SDEs) to generate data from noise. Although diffusion models have achieved remarkable progress in recent years, they have limitations in the unpaired image-to-image translation tasks due to the Gaussian prior assumption. Schr\"odinger Bridge (SB), which learns an SDE to translate between two arbitrary distributions, have risen as an attractive solution to this problem. However, none of SB models so far have been successful at unpaired translation between high-resolution images. In this work, we propose the Unpaired Neural Schr\"odinger Bridge (UNSB), which expresses 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 demonstrate that UNSB is scalable and successfully solves various unpaired image-to-image translation tasks. Code: \url{https://github.com/cyclomon/UNSB}

翻译：扩散模型是一类强大的生成模型，通过模拟随机微分方程从噪声中生成数据。尽管扩散模型近年来取得了显著进展，但由于其高斯先验假设，在非配对图像到图像翻译任务中存在局限性。薛定谔桥通过学习在两个任意分布之间进行转换的随机微分方程，已成为解决此问题的有吸引力方案。然而，目前尚无薛定谔桥模型能成功实现高分辨率图像间的非配对翻译。在本文中，我们提出非配对神经薛定谔桥（UNSB），将薛定谔桥问题表述为一系列对抗性学习问题。这使我们能够整合先进的判别器和正则化方法，学习非配对数据之间的薛定谔桥。我们证明UNSB具有可扩展性，并能成功解决各种非配对图像到图像翻译任务。代码：\url{https://github.com/cyclomon/UNSB}