用期望最大化训练扩散模型，从噪声数据中恢复清晰图像。
DiffEM: Learning from Corrupted Data with Diffusion Models via Expectation Maximization
- E步用条件扩散模型重建干净数据，M步用重建结果优化模型
- 在图像重建任务中显著提升噪声数据下的生成质量
- 适合处理标注不全或观测受损的生成学习场景
扩散模型已成为高维逆问题的强大生成先验，但在仅有噪声或损坏观测的情况下进行训练仍具挑战。本文提出一种基于期望最大化（EM）的扩散模型训练新方法——DiffEM。在E步中，利用条件扩散模型从观测数据中重建干净样本；在M步中，使用重建结果来优化条件扩散模型。理论上，在满足适当统计条件下，我们证明了DiffEM迭代具有单调收敛性。实验表明，该方法在多种图像重建任务中均有效，能从噪声数据中学习高质量生成模型。
原文摘要 · Abstract (English)
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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