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 combines SB with adversarial training and regularization to learn a SB between unpaired data. We demonstrate that UNSB is scalable, and that it 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，它们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，但由于高斯先验假设，它们在无配对图像到图像翻译任务中存在局限性。薛定谔桥通过学习一个能在任意两个分布之间翻译的随机微分方程，已成为解决该问题的有吸引力的方案。然而，迄今为止尚无薛定谔桥模型能成功实现高分辨率图像的无配对翻译。在本工作中，我们提出了无配对神经薛定谔桥（UNSB），该方法将薛定谔桥与对抗训练及正则化相结合，以学习无配对数据之间的薛定谔桥。我们展示了UNSB具有良好的可扩展性，并能成功解决多种无配对图像到图像翻译任务。代码：\url{https://github.com/cyclomon/UNSB}