Diffusion and interpolation-based conditional generative models : theoretical analysis and applications
Type: dissertation
Abstract: Recent advances in deep generative learning have enabled powerful new approaches for statistical modeling, with the conditional generative paradigm in particular serving as a highly scalable tool for quantifying complex relationships in high-dimensional data.Within this domain, process-based methods, such as diffusion and interpolation models, have emerged as the state-of-the-art.However, significant theoretical and practical challenges remain.This dissertation addresses these critical issues across the spectrum of theory, methodology, and scientific application to advance the capabilities of process-based conditional generation.First, we construct a rigorous theoretical foundation for this model class.We introduce the Conditional Stochastic Interpolation (CSI) framework, which provides a unified mathematical treatment for both Ordinary Differential Equation (ODE) and Stochastic Differential Equation (SDE) based samplers.In our proposed approach, we incorporate an adaptive diffusion term to address the instability issues arising in the diffusion process.We derive explicit expressions of the conditional drift and score functions in terms of conditional expectations, which natui
Author: Huang, Ding
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7217953282