Multi-feature Reconstruction Network using Crossed-mask Restoration for Unsupervised Anomaly Detection

arXiv:2404.13273v1 Announce Type: cross
Abstract: Unsupervised anomaly detection using only normal samples is of great significance for quality inspection in industrial manufacturing. Although existing reconstruction-based methods have achieved promising results, they still face two problems: poor distinguishable information in image reconstruction and well abnormal regeneration caused by model over-generalization ability. To overcome the above issues, we convert the image reconstruction into a combination of parallel feature restorations and propose a multi-feature reconstruction network, MFRNet, using crossed-mask restoration in this paper. …

abstract anomaly anomaly detection arxiv cs.cv cs.lg detection face feature image industrial industrial manufacturing information manufacturing network normal quality restoration results samples significance type unsupervised