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arXiv Machine Learning By Alexandros Graikos, Esmeralda S. Whitammer, Nebojsa Jojic, Dimitris Samaras

Diffusion models as plug-and-play priors

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The paper explores using denoising diffusion generative models as plug‑and‑play priors for high‑dimensional inference problems. By combining a pre‑trained diffusion prior with a differentiable auxiliary constraint, the authors enable approximate inference through iterative differentiation across multiple noisy versions of the data. This framework opens possibilities for conditional generation, image segmentation, and novel combinatorial optimization algorithms.

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