Schödinger Bridge Type Diffusion Models as an Extension of Variational Autoencoders

• URL: http://arxiv.org/abs/2412.18237v1
• Date: Tue, 24 Dec 2024 07:43:14 GMT
• Title: Schödinger Bridge Type Diffusion Models as an Extension of Variational Autoencoders
• Authors: Kentaro Kaba, Reo Shimizu, Masayuki Ohzeki, Yuki Sughiyama,
• Abstract summary: We propose a unified framework to construct diffusion models by reinterpreting the SB-type models as an extension of variational autoencoders. We find that the objective function consists of the prior loss and drift matching parts.
• Score: 0.4499833362998489
• License:
• Abstract: Generative diffusion models use time-forward and backward stochastic differential equations to connect the data and prior distributions. While conventional diffusion models (e.g., score-based models) only learn the backward process, more flexible frameworks have been proposed to also learn the forward process by employing the Schr\"odinger bridge (SB). However, due to the complexity of the mathematical structure behind SB-type models, we can not easily give an intuitive understanding of their objective function. In this work, we propose a unified framework to construct diffusion models by reinterpreting the SB-type models as an extension of variational autoencoders. In this context, the data processing inequality plays a crucial role. As a result, we find that the objective function consists of the prior loss and drift matching parts.

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