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

2026-07-02

AI Summary

This work addresses the challenges of industrial anomaly detection in cold-start scenarios, where scarce normal samples and limited anomalies lead to loose normal boundaries and underutilized supervisory signals. To this end, the authors propose ArcAD, a plug-and-play calibration framework that leverages hyperspherical embedding and a push-pull learning paradigm. ArcAD aggregates the limited normal samples into a compact cluster to better cover the normal manifold while simultaneously contracting the decision boundary inward using both real and synthetically generated anomalies, thereby sharpening the boundary and enhancing discriminability. Notably, ArcAD is the first method to effectively model the normal distribution and fully exploit anomaly supervision under cold-start conditions. It achieves state-of-the-art performance across multiple benchmarks—including MVTec-AD, VisA, Real-IAD, and MANTA—outperforming both supervised and unsupervised baselines in both single-class and multi-class cold-start settings.

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.

Problem

• cold-start
• anomaly detection
• industrial inspection
• data scarcity
• supervised learning

Innovation

• cold-start anomaly detection
• push-pull learning
• hypersphere clustering
• pseudo-anomaly synthesis
• normal boundary calibration