May 9, 2024, 4:41 a.m. | Wei Deng, Weijian Luo, Yixin Tan, Marin Bilo\v{s}, Yu Chen, Yuriy Nevmyvaka, Ricky T. Q. Chen

cs.LG updates on

arXiv:2405.04795v1 Announce Type: new
Abstract: Schr\"odinger bridge (SB) has emerged as the go-to method for optimizing transportation plans in diffusion models. However, SB requires estimating the intractable forward score functions, inevitably resulting in the costly implicit training loss based on simulated trajectories. To improve the scalability while preserving efficient transportation plans, we leverage variational inference to linearize the forward score functions (variational scores) of SB and restore simulation-free properties in training backward scores. We propose the variational Schr\"odinger diffusion model …

abstract arxiv bridge cs.lg diffusion diffusion models functions however inference loss scalability training training loss transportation type while

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