DiffEM: Learning from Corrupted Data with Diffusion Models via Expectation Maximization
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Image ReconstructionSimilar Papers 제목 키워드 기반
An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations
Diffusion models excel in solving imaging inverse problems due to their ability to model complex image priors. However, their reliance on large, clean datasets for training limits their practical use where clean data is …
DeblurringDenoisingLearning Diffusion Model from Noisy Measurement using Principled Expectation-Maximization Method
Diffusion models have demonstrated exceptional ability in modeling complex image distributions, making them versatile plug-and-play priors for solving imaging inverse problems. However, their reliance on large-scale clea…
DeblurringDenoisingRobust High Dimensional Expectation Maximization Algorithm via Trimmed Hard Thresholding
In this paper, we study the problem of estimating latent variable models with arbitrarily corrupted samples in high dimensional space ({\em i.e.,} $d\gg n$) where the underlying parameter is assumed to be sparse. Specifi…
Vocal Bursts Intensity PredictionDiffRetriever: Parallel Representative Tokens for Retrieval with Diffusion Language Models
This paper shows how diffusion language models (DLMs) can be used as effective and efficient retrievers. Existing DLM-based retrievers (e.g., DiffEmbed) follow BERT-style encoding, representing each query or passage as a…
Adaptive Robust Learning using Latent Bernoulli Variables
We present an adaptive approach for robust learning from corrupted training sets. We identify corrupted and non-corrupted samples with latent Bernoulli variables and thus formulate the learning problem as maximization of…
Variational Inference