Generalized Schr\"odinger Bridge Matching

arXiv:2310.02233v2 Announce Type: replace-cross
Abstract: Modern distribution matching algorithms for training diffusion or flow models directly prescribe the time evolution of the marginal distributions between two boundary distributions. In this work, we consider a generalized distribution matching setup, where these marginals are only implicitly described as a solution to some task-specific objective function. The problem setup, known as the Generalized Schr\"odinger Bridge (GSB), appears prevalently in many scientific areas both within and without machine learning. We propose Generalized Schr\"odinger Bridge …

arxiv bridge cs.lg generalized math.oc stat.ml type