Anomaly-score calibration under severe class imbalance

Improve anomaly-score calibration for industrial anomaly detection and pixel-level localization under severe class imbalance, where relatively low average precision remains an issue.

Background

NC-TFAD achieves strong AUROC-based detection and localization results on MVTec AD and VisA, but its average precision is comparatively low in settings with substantial normal–anomaly or pixel-level class imbalance. The paper attributes this limitation to false-positive predictions receiving a disproportionate effect on precision–recall performance, particularly for small or sparse defects.

The authors explicitly identify finer anomaly-score calibration as unresolved. Such calibration is intended to suppress high-confidence false positives and improve precision-oriented evaluation without undermining the method’s ability to rank anomalous images or pixels effectively. The issue is presented as relevant both to image-level anomaly detection and to pixel-level localization.