DiffEM: Learning from Corrupted Data with Diffusion Models via Expectation Maximization
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918256575512576 |
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| author | Hosseintabar, Danial Chen, Fan Daras, Giannis Torralba, Antonio Daskalakis, Constantinos |
| author_facet | Hosseintabar, Danial Chen, Fan Daras, Giannis Torralba, Antonio Daskalakis, Constantinos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12691 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiffEM: Learning from Corrupted Data with Diffusion Models via Expectation Maximization Hosseintabar, Danial Chen, Fan Daras, Giannis Torralba, Antonio Daskalakis, Constantinos Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | DiffEM: Learning from Corrupted Data with Diffusion Models via Expectation Maximization |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.12691 |