Çağatay Ersin

Lightweight One-Class Industrial Anomaly Detection Using a Normal-Calibrated Random-Subspace Ensemble

Industrial anomaly detection is commonly performed under one-class conditions because defective samples are scarce or unavailable during training. This study presents a lightweight one-class anomaly detection framework that reduces the dependence of compact deep representations on a single arbitrarily selected feature...