ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection

The deployment of Industrial Anomaly Detection (IAD) inreal-world manufacturing frequently encounters a challenging cold-startbottleneck, in which limited normal samples fail to represent the fullnormal distribution and only a few anomalies are available. Under sucha regime, existing methods struggle to form compact normal boundariesand fail to effectively exploit supervised signals from rare defects. To ad-dress this challenge, we propose Anomaly-Rectified Cold-start AD (Ar-cAD), a plug-and-play calibration framework for reconstruction-basedIAD baselines. ArcAD follows a push–pull learning paradigm to constructa compact and discriminative normal boundary under data scarcity. Onthe one hand, ArcAD projects limited normal samples onto a hypersphereand pulls them into multiple compact clusters to maximize coverage ofthe normal manifold. On the other hand, it synthesizes pseudo-anomalieson the hypersphere and leverages real anomalies to push the boundaryinward and sharpen anomaly discrimination. Extensive experiments onMVTec-AD, VisA, Real-IAD, and MANTA demonstrate that ArcADsignificantly outperforms state-of-the-art supervised and unsupervisedmethods in both single-class and multi-class settings under cold-startconditions. Code is available at: https://github.com/LGC-AD/ArcAD.