{"ID":2855849,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.12691","arxiv_id":"2510.12691","title":"DiffEM: Learning from Corrupted Data with Diffusion Models via Expectation Maximization","abstract":"Diffusion models have emerged as powerful generative priors for high-dimensional inverse problems, yet learning them when only corrupted or noisy observations are available remains challenging. In this work, we propose a new method for training diffusion models with Expectation-Maximization (EM) from corrupted data. Our proposed method, DiffEM, utilizes conditional diffusion models to reconstruct clean data from observations in the E-step, and then uses the reconstructed data to refine the conditional diffusion model in the M-step. Theoretically, we provide monotonic convergence guarantees for the DiffEM iteration, assuming appropriate statistical conditions. We demonstrate the effectiveness of our approach through experiments on various image reconstruction tasks.","short_abstract":"Diffusion models have emerged as powerful generative priors for high-dimensional inverse problems, yet learning them when only corrupted or noisy observations are available remains challenging. In this work, we propose a new method for training diffusion models with Expectation-Maximization (EM) from corrupted data. Ou...","url_abs":"https://arxiv.org/abs/2510.12691","url_pdf":"https://arxiv.org/pdf/2510.12691v3","authors":"[\"Danial Hosseintabar\",\"Fan Chen\",\"Giannis Daras\",\"Antonio Torralba\",\"Constantinos Daskalakis\"]","published":"2025-10-14T16:25:02Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}