SetAD: Semi-Supervised Anomaly Learning in Contextual Sets

• URL: http://arxiv.org/abs/2512.07863v1
• Date: Wed, 26 Nov 2025 13:27:59 GMT
• Title: SetAD: Semi-Supervised Anomaly Learning in Contextual Sets
• Authors: Jianling Gao, Chongyang Tao, Xuelian Lin, Junfeng Liu, Shuai Ma,
• Abstract summary: Semi-supervised anomaly detection has shown great promise by effectively leveraging limited labeled data.<n>We propose SetAD, a novel framework that reframes semi-supervised AD as a Set-level Anomaly Detection task.<n>To enhance robustness and score calibration, we propose a context-calibrated anomaly scoring mechanism.
• Score: 25.628827917857603
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semi-supervised anomaly detection (AD) has shown great promise by effectively leveraging limited labeled data. However, existing methods are typically structured around scoring individual points or simple pairs. Such {point- or pair-centric} view not only overlooks the contextual nature of anomalies, which are defined by their deviation from a collective group, but also fails to exploit the rich supervisory signals that can be generated from the combinatorial composition of sets. Consequently, such models struggle to exploit the high-order interactions within the data, which are critical for learning discriminative representations. To address these limitations, we propose SetAD, a novel framework that reframes semi-supervised AD as a Set-level Anomaly Detection task. SetAD employs an attention-based set encoder trained via a graded learning objective, where the model learns to quantify the degree of anomalousness within an entire set. This approach directly models the complex group-level interactions that define anomalies. Furthermore, to enhance robustness and score calibration, we propose a context-calibrated anomaly scoring mechanism, which assesses a point's anomaly score by aggregating its normalized deviations from peer behavior across multiple, diverse contextual sets. Extensive experiments on 10 real-world datasets demonstrate that SetAD significantly outperforms state-of-the-art models. Notably, we show that our model's performance consistently improves with increasing set size, providing strong empirical support for the set-based formulation of anomaly detection.

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