Related papers: Riemannian Diffusion Schr\"odinger Bridge

Riemannian Diffusion Schr\"odinger Bridge

URL: http://arxiv.org/abs/2207.03024v1
Date: Thu, 7 Jul 2022 00:35:04 GMT
Title: Riemannian Diffusion Schr\"odinger Bridge
Authors: James Thornton, Michael Hutchinson, Emile Mathieu, Valentin De Bortoli, Yee Whye Teh, Arnaud Doucet
Abstract summary: We introduce emphRiemannian Diffusion Schr"odinger Bridge to accelerate sampling of diffusion models. We validate our proposed method on synthetic data and real Earth and climate data.
Score: 56.20669989459281
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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 Schr\"odinger Bridge}. Our proposed method generalizes Diffusion Schr\"odinger 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.

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