PTAD: Prototype-Oriented Tabular Anomaly Detection via Mask Modeling
Keywords: Tabular anomaly detection, Prototype learning, Mask modeling
Abstract: Tabular anomaly detection, which aims at identifying deviant samples, has been crucial in a variety of real-world applications, such as medical disease identification, financial fraud detection, intrusion monitoring, etc. Although recent deep learning-based methods have achieved competitive performances, these methods suffer from representation entanglement and the lack of global correlation modeling, which leads to the 'abnormal leakage' issue and hinders anomaly detection performance. To tackle the problem, we incorporate mask modeling and prototype learning into tabular anomaly detection. The core idea is to design learnable masks by disentangled representation learning within a projection space and extracting nominal dependencies as explicit global prototypes. Specifically, the overall model involves two parts: (i) During encoding, we perform mask modeling in both the data space and projection space with orthogonal basis vectors for masking out the suspicious abnormal locations; (ii) During decoding, we decode multiple masked representations in parallel for reconstruction and learn association prototypes to extract nominal characteristic correlations. Our proposal derives from a distribution-matching perspective, where both projection space learning and association prototype learning are formulated as optimal transport problems, and the calibration distances are utilized to refine the anomaly scores. By conducting both quantitative and qualitative experiments on 20 tabular benchmarks, our model surpasses other competitors and possesses good interpretability.
Supplementary Material: zip
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 7039
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