Subspace-Guided Feature Reconstruction for Unsupervised Anomaly Localization

arXiv:2309.13904v2 Announce Type: replace
Abstract: Unsupervised anomaly localization, which plays a critical role in industrial manufacturing, aims to identify anomalous regions that deviate from normal sample patterns. Most recent methods perform feature matching or reconstruction for the target sample with pre-trained deep neural networks. However, they still struggle to address challenging anomalies because the deep embeddings stored in the memory bank can be less powerful and informative. More specifically, prior methods often overly rely on the finite resources stored in …

abstract anomaly arxiv cs.cv feature identify industrial industrial manufacturing localization manufacturing networks neural networks normal patterns role sample struggle type unsupervised