Diffusion Schr\"odinger Bridge Matching

• URL: http://arxiv.org/abs/2303.16852v3
• Date: Tue, 12 Dec 2023 00:49:53 GMT
• Title: Diffusion Schr\"odinger Bridge Matching
• Authors: Yuyang Shi, Valentin De Bortoli, Andrew Campbell, Arnaud Doucet
• Abstract summary: We introduce Iterative Markovian Fitting (IMF) and Diffusion Schr"odinger Bridge Matching (DSBM) IMF is a new methodology for solving SB problems, and DSBM is a novel numerical algorithm for computing IMF iterates. We demonstrate the performance of DSBM on a variety of problems.
• Score: 36.95088080680221
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Solving transport problems, i.e. finding a map transporting one given distribution to another, has numerous applications in machine learning. Novel mass transport methods motivated by generative modeling have recently been proposed, e.g. Denoising Diffusion Models (DDMs) and Flow Matching Models (FMMs) implement such a transport through a Stochastic Differential Equation (SDE) or an Ordinary Differential Equation (ODE). However, while it is desirable in many applications to approximate the deterministic dynamic Optimal Transport (OT) map which admits attractive properties, DDMs and FMMs are not guaranteed to provide transports close to the OT map. In contrast, Schr\"odinger bridges (SBs) compute stochastic dynamic mappings which recover entropy-regularized versions of OT. Unfortunately, existing numerical methods approximating SBs either scale poorly with dimension or accumulate errors across iterations. In this work, we introduce Iterative Markovian Fitting (IMF), a new methodology for solving SB problems, and Diffusion Schr\"odinger Bridge Matching (DSBM), a novel numerical algorithm for computing IMF iterates. DSBM significantly improves over previous SB numerics and recovers as special/limiting cases various recent transport methods. We demonstrate the performance of DSBM on a variety of problems.

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