黎曼扩散薛定谔桥
Riemannian Diffusion Schrödinger Bridge
摘要：
基于分数的生成模型在密度估计和生成建模任务上表现出最先进的性能。这些模型通常假设数据的几何形状是平坦的，但最近的扩展已经发展到合成黎曼流形上的数据。现有的方法来加速采样的扩散模型通常不适用于黎曼设置和黎曼分数为基础的方法尚未适应的重要任务的数据集插值。为了克服这些问题，我们引入了\emph{Riemannian Diffusion Schrödinger Bridge}。我们提出的方法推广扩散薛定谔桥引入\cite{debortoli2021neurips}的非欧设置和扩展黎曼分数为基础的模型超出了第一次的时间反转。我们验证了我们提出的方法的合成数据和真实的地球和气候数据。
英文摘要：
Score-based generative models exhibit state of the art performance on density estimation and generative modeling tasks. These models typically assume that the data geometry is flat, yet recent extensions have been developed to synthesize data living on Riemannian manifolds. Existing methods to accelerate sampling of diffusion models are typically not applicable in the Riemannian setting and Riemannian score-based methods have not yet been adapted to the important task of interpolation of datasets. To overcome these issues, we introduce \emph{Riemannian Diffusion Schrödinger Bridge}. Our proposed method generalizes Diffusion Schrödinger Bridge introduced in \cite{debortoli2021neurips} to the non-Euclidean setting and extends Riemannian score-based models beyond the first time reversal. We validate our proposed method on synthetic data and real Earth and climate data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
