arXiv Machine Learning
A Gradient Flow Approach to Solving Inverse Problems with Latent Diffusion Models
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Exact Evaluation of the Accuracy of Diffusion Models for Inverse Problems with Gaussian Data Distributions
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Diffusion Based Unpaired Data Learning for Inverse Problems
The paper introduces LUD-DIF, a diffusion-based method that solves inverse problems using unpaired data. By deriving the evidence lower bound of the joint distribution and decoupling it into two independent diffusion processes under a weak‑coupling assumption, the authors provide a variational inference framework, a loss function, and an error‑bound analysis. Experiments show that LUD‑DIF performs well across multiple image inverse problems, demonstrating its effectiveness and generalization in unpaired settings.
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