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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.13712 |
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| _version_ | 1866914127146909696 |
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| author | Rozet, François Andry, Gérôme Lanusse, François Louppe, Gilles |
| author_facet | Rozet, François Andry, Gérôme Lanusse, François Louppe, Gilles |
| contents | Diffusion models recently proved to be remarkable priors for Bayesian inverse problems. However, training these models typically requires access to large amounts of clean data, which could prove difficult in some settings. In this work, we present DiEM, a novel method based on the expectation-maximization algorithm for training diffusion models from incomplete and noisy observations only. Unlike previous works, DiEM leads to proper diffusion models, which is crucial for downstream tasks. As part of our methods, we propose and motivate an improved posterior sampling scheme for unconditional diffusion models. We present empirical evidence supporting the effectiveness of our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_13712 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Diffusion Priors from Observations by Expectation Maximization Rozet, François Andry, Gérôme Lanusse, François Louppe, Gilles Machine Learning Diffusion models recently proved to be remarkable priors for Bayesian inverse problems. However, training these models typically requires access to large amounts of clean data, which could prove difficult in some settings. In this work, we present DiEM, a novel method based on the expectation-maximization algorithm for training diffusion models from incomplete and noisy observations only. Unlike previous works, DiEM leads to proper diffusion models, which is crucial for downstream tasks. As part of our methods, we propose and motivate an improved posterior sampling scheme for unconditional diffusion models. We present empirical evidence supporting the effectiveness of our approach. |
| title | Learning Diffusion Priors from Observations by Expectation Maximization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.13712 |