DiffEM: Learning from Corrupted Data with Diffusion Models via Expectation Maximization

• URL: http://arxiv.org/abs/2510.12691v1
• Date: Tue, 14 Oct 2025 16:25:02 GMT
• Title: DiffEM: Learning from Corrupted Data with Diffusion Models via Expectation Maximization
• Authors: Danial Hosseintabar, Fan Chen, Giannis Daras, Antonio Torralba, Constantinos Daskalakis,
• Abstract summary: We propose a new method for training diffusion models with Expectation-Maximization (EM) from corrupted data.<n>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.
• Score: 42.67146690499833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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