Learning Anomaly-Aware Normal Subspaces for Few-Shot Industrial Anomaly Detection

Few-shot industrial anomaly detection aims to identify unseen defects us- ing only a few normal images. Recent reconstruction-based methods seek to model normality through feature restoration. However, they may fail to generalize to unseen normal variations or inadvertently reconstruct anoma- lous information, leading to false positives and false negatives.To address this issue, we propose NormProj, which learns an anomaly-aware normal subspace by determining which feature variations should be preserved or re- jected. An orthogonal-basis generator constructs the normal subspace from augmented support features, while joint normal reconstruction and paired synthetic-anomaly restoration encourage the preservation of legitimate nor- mal variations and the exclusion of anomaly-induced changes. After train- ing, the generator is discarded, enabling efficient projection-based anomaly scoring without an additional neural reconstruction network. Experiments on MVTec-AD and VisA demonstrate improved image-level anomaly de- tection over reconstruction- and PCA-based baselines, together with com- petitive localization performance across multiple few-shot settings. Code is available at https://github.com/sfhiswfgh/NormProj.