Ufuk Efe

Robust Logical Anomaly Detection under Capture Shifts via Synthetic Anomalies

A framework in which a classifier, trained on normal images and on synthetic anomalies, scores a test image without comparing it with normal data is proposed, suggesting that clean benchmark scores can hide a strong sensitivity to capture conditions, and that learning the decision from both normal images and rule viola...