Unsupervised Anomaly Detection via Masked Diffusion Posterior Sampling

arXiv:2404.17900v1 Announce Type: new
Abstract: Reconstruction-based methods have been commonly used for unsupervised anomaly detection, in which a normal image is reconstructed and compared with the given test image to detect and locate anomalies. Recently, diffusion models have shown promising applications for anomaly detection due to their powerful generative ability. However, these models lack strict mathematical support for normal image reconstruction and unexpectedly suffer from low reconstruction quality. To address these issues, this paper proposes a novel and highly-interpretable method …

abstract anomaly anomaly detection applications arxiv cs.cv detection diffusion diffusion models generative however image normal posterior sampling test type unsupervised via