aiqbench analysis
This research demonstrates that inverse solvers can reliably utilize mismatched or low-fidelity diffusion priors when measurements are highly informative, challenging the assumption that high-fidelity, in-domain models are strictly necessary. By combining Bayesian-consistency theory with local-correlation analysis, the study identifies specific regimes where weak priors succeed due to shared spatial structures, providing a principled framework for deploying more accessible generative models. These findings offer practical guidance for optimizing inverse problem solutions in scenarios where training data alignment is limited.
Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performance
arXiv:2601.22443v2 Announce Type: replace Abstract: Can a diffusion model trained on bedrooms recover human faces? Diffusion models are widely used as priors for inverse problems, but standard approaches usually assume a high-fidelity model trained on data that closely match the unknown signal. In practice, one often must use a mismatched or low-fidelity diffusion prior. Surprisingly, these weak priors often perform nearly as well as full-strength, in-domain baselines. We study when and why inverse solvers are robust to weak diffusion priors. Through extensive experiments, we find that weak priors succeed when measurements are highly informative (e.g., many observed pixels), and we identify regimes where they fail. To explain this behavior, we combine Bayesian-consistency theory with local-correlation analysis: the theory gives conditions under which high-dimensional measurements make the posterior concentrate near the true signal, while the correlation analysis shows that weak and stronger natural-image priors can share similar local spatial structure. These results provide a principled justification on when weak diffusion priors can be used reliably. Code is available at https://github.com/jjia131/weak-diffusion-priors-inverse-problem.
Abstract:Can a diffusion model trained on bedrooms recover human faces? Diffusion models are widely used as priors for inverse problems, but standard approaches usually assume a high-fidelity model trained on data that closely match the unknown signal. In practice, one often must use a mismatched or low-fidelity diffusion prior. Surprisingly, these weak priors often perform nearly as well as full-strength, in-domain baselines. We study when and why inverse solvers are robust to weak diffusion priors. Through extensive experiments, we find that weak priors succeed when measurements are highly informative (e.g., many observed pixels), and we identify regimes where they fail. To explain this behavior, we combine Bayesian-consistency theory with local-correlation analysis: the theory gives conditions under which high-dimensional measurements make the posterior concentrate near the true signal, while the correlation analysis shows that weak and stronger natural-image priors can share similar local spatial structure. These results provide a principled justification on when weak diffusion priors can be used reliably. Code is available at this https URL.
37 pages, ICML 2026 spotlight. Code: this https URL, Project Page: this https URL
Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Computation (stat.CO); Machine Learning (stat.ML)
arXiv:2601.22443 [cs.LG]
(or arXiv:2601.22443v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2601.22443 Focus to learn more arXiv-issued DOI via DataCite
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