Latent Schr{ö}dinger Bridge Diffusion Model for Generative Learning

• URL: http://arxiv.org/abs/2404.13309v2
• Date: Tue, 15 Oct 2024 07:09:11 GMT
• Title: Latent Schr{ö}dinger Bridge Diffusion Model for Generative Learning
• Authors: Yuling Jiao, Lican Kang, Huazhen Lin, Jin Liu, Heng Zuo,
• Abstract summary: We introduce a novel generative learning methodology utilizing the Schr"odinger bridge diffusion model in latent space. We develop a diffusion model within the latent space utilizing the Schr"odinger bridge framework.
• Score: 7.13080924844185
• License:
• Abstract: This paper aims to conduct a comprehensive theoretical analysis of current diffusion models. We introduce a novel generative learning methodology utilizing the Schr{\"o}dinger bridge diffusion model in latent space as the framework for theoretical exploration in this domain. Our approach commences with the pre-training of an encoder-decoder architecture using data originating from a distribution that may diverge from the target distribution, thus facilitating the accommodation of a large sample size through the utilization of pre-existing large-scale models. Subsequently, we develop a diffusion model within the latent space utilizing the Schr{\"o}dinger bridge framework. Our theoretical analysis encompasses the establishment of end-to-end error analysis for learning distributions via the latent Schr{\"o}dinger bridge diffusion model. Specifically, we control the second-order Wasserstein distance between the generated distribution and the target distribution. Furthermore, our obtained convergence rates effectively mitigate the curse of dimensionality, offering robust theoretical support for prevailing diffusion models.

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