{"ID":5675411,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-07T01:06:03.009715918Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.02252","arxiv_id":"2607.02252","title":"ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection","abstract":"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 one hand, ArcAD projects limited normal samples onto a hypersphere and pulls them into multiple compact clusters to maximize coverage of the normal manifold. On the other hand, it synthesizes pseudo-anomalies on the hypersphere and leverages real anomalies to push the boundary inward and sharpen anomaly discrimination. Extensive experiments on MVTec-AD, VisA, Real-IAD, and MANTA demonstrate that ArcAD significantly outperforms state-of-the-art supervised and unsupervised methods in both single-class and multi-class settings under cold-start conditions. Code is available at: https://github.com/LGC-AD/ArcAD.","short_abstract":"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 compac...","url_abs":"https://arxiv.org/abs/2607.02252","url_pdf":"https://arxiv.org/pdf/2607.02252v1","authors":"[\"Ningning Han\",\"Lei Fan\",\"Jia Guo\",\"Yunkang Cao\",\"Xiu Su\",\"Feng Cao\",\"Donglin Di\",\"Tonghua Su\"]","published":"2026-07-02T14:43:21Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":613901,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-03T01:40:09.565152011Z","DeletedAt":null,"paper_id":5675411,"paper_url":"https://arxiv.org/abs/2607.02252","paper_title":"ArcAD: Anomaly-Rectified Calibration for Cold-Start Supervised Anomaly Detection","repo_url":"https://github.com/LGC-AD/ArcAD","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}