GRef: Graph-Guided Reference Selection for Generalizable Industrial Anomaly Detection

Frequent product changes make repeated training of industrial anomaly detectors costly, motivating methods that generalize to unseen products using normal reference images. When the available normal pool exceeds the reference budget, detection performance depends on which images are selected. Retrieving the most similar images individually can produce redundant references that provide limited complementary evidence. We propose GRef, a graph-guided framework that combines transferable relational representations with reference selection balancing query relevance and redundancy. It supports both selector-guided detector training and plug-in inference without target-side parameter updates. We establish a generalization bound that accounts for discrete reference selection, relating detection error to selection margins and model complexity. Across six industrial datasets, ResAD++ trained with our selected references achieves an average Image AUROC of 87.94%, outperforming all compared learned detectors. Plug-in integration with UniVAD achieves the best overall average of 93.70%. With detector parameters fixed, GRef also achieves the highest average performance among the compared selection strategies for all four evaluated detectors.