ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
The deployment of Industrial Anomaly Detection (IAD) in real-world manufacturing frequently encounters a challenging cold-start bottleneck, in which limited normal samples fail to represent the full normal distribution and only a few anomalies are available. Under such a regime, existing methods struggle to form compact normal boundaries and fail to effectively exploit supervised signals from rare defects. To address this challenge, we propose Anomaly-Rectified Cold-start AD (ArcAD), a plug-and-play calibration framework for reconstruction-based IAD baselines. ArcAD follows a push-pull learning paradigm to construct a compact and discriminative normal boundary under data scarcity. On the ...
| Authors | Ningning Han, Lei Fan, Jia Guo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 July 2026 |
| Last updated at source | 2 August 2026 |
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