Deviation‐Guided Attention for Semi‐Supervised Anomaly Detection With Contrastive Regularisation

ABSTRACT Anomaly detection (AD) aims to identify abnormal patterns that deviate from normal behaviour, playing a critical role in applications such as industrial inspection, medical imaging and autonomous driving. However, AD often faces a scarcity of labelled data. To address this challenge, we propose a novel semi‐supervised anomaly detection method, DASAD (Deviation‐Guided Attention for Semi‐Supervised Anomaly Detection), which integrates deviation‐guided attention with contrastive regularisation to reduce the unreliability of pseudo‐labels. Specifically, a deviation‐guided attention mechanism is designed to combine three types of deviations: latent embeddings, residual direction vectors and hierarchical reconstruction errors to capture anomaly specific cues effectively, thereby enhancing the credibility of pseudo‐labels for unlabelled samples. Furthermore, a class‐asymmetric contrastive loss is constructed to promote compact representations of normal instances while preserving the structural diversity of anomalies. Extensive experiments on 8 benchmark datasets demonstrate that DASAD consistently outperforms state‐of‐the‐art methods and exhibits strong generalisation across 6 anomaly detection domains.