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 learnin
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorNingning Han →
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
- Linked via arxiv authorLei Fan →
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
- Linked via arxiv authorJia Guo →
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
- Linked via arxiv authorYunkang Cao →
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
- Linked via arxiv authorXiu Su →
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
- Linked via arxiv authorFeng Cao →
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
- Linked via arxiv authorDonglin Di →
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection
- Linked via arxiv authorTonghua Su →
ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection