Towards One-for-All Anomaly Detection for Tabular Data

• URL: http://arxiv.org/abs/2603.14407v1
• Date: Sun, 15 Mar 2026 14:42:05 GMT
• Title: Towards One-for-All Anomaly Detection for Tabular Data
• Abstract summary: Tabular anomaly detection (TAD) is critical in many real-world applications.<n>We propose OFA-TAD, a framework that only requires one-time training on multiple source datasets.<n>Experiments on 34 datasets from 14 domains demonstrate that OFA-TAD achieves superior anomaly detection performance.
• Score: 87.63505963517512
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tabular anomaly detection (TAD) aims to identify samples that deviate from the majority in tabular data and is critical in many real-world applications. However, existing methods follow a ``one model for one dataset (OFO)'' paradigm, which relies on dataset-specific training and thus incurs high computational cost and yields limited generalization to unseen domains. To address these limitations, we propose OFA-TAD, a generalist one-for-all (OFA) TAD framework that only requires one-time training on multiple source datasets and can generalize to unseen datasets from diverse domains on-the-fly. To realize one-for-all tabular anomaly detection, OFA-TAD extracts neighbor-distance patterns as transferable cues, and introduces multi-view neighbor-distance representations from multiple transformation-induced metric spaces to mitigate the transformation sensitivity of distance profiles. To adaptively combine multi-view distance evidence, a Mixture-of-Experts (MoE) scoring network is employed for view-specific anomaly scoring and entropy-regularized gated fusion, with a multi-strategy anomaly synthesis mechanism to support training under the one-class constraint. Extensive experiments on 34 datasets from 14 domains demonstrate that OFA-TAD achieves superior anomaly detection performance and strong cross-domain generalizability under the strict OFA setting.

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