Learning Diffusion Priors from Observations by Expectation Maximization

• URL: http://arxiv.org/abs/2405.13712v5
• Date: Fri, 31 Oct 2025 19:47:46 GMT
• Title: Learning Diffusion Priors from Observations by Expectation Maximization
• Authors: François Rozet, Gérôme Andry, François Lanusse, Gilles Louppe,
• Abstract summary: We present DiEM, a novel method for training diffusion models from incomplete and noisy observations only.<n>Unlike previous works, DiEM leads to proper diffusion models, which is crucial for downstream tasks.
• Score: 10.704978219090039
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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