面向表格数据的通用异常检测
Towards One-for-All Anomaly Detection for Tabular Data
AI总结：
提出OFA-TAD框架，通过多视图邻居距离表示和混合专家评分网络，实现跨领域表格异常检测的通用化，一次训练即可泛化到未见数据集。
摘要：
表格异常检测（TAD）旨在识别表格数据中偏离大多数样本的样本，在许多实际应用中至关重要。然而，现有方法遵循“一个数据集一个模型（OFO）”范式，依赖于数据集特定的训练，导致计算成本高且对未见领域的泛化能力有限。为解决这些局限性，我们提出OFA-TAD，一个通用的“一劳永逸（OFA）”TAD框架，只需在多个源数据集上进行一次训练，即可即时泛化到来自不同领域的未见数据集。为实现通用表格异常检测，OFA-TAD提取邻居距离模式作为可迁移线索，并引入来自多个变换诱导度量空间的多视图邻居距离表示，以减轻距离分布对变换的敏感性。为自适应组合多视图距离证据，采用混合专家（MoE）评分网络进行视图特定异常评分和熵正则化门控融合，并采用多策略异常合成机制以支持单类约束下的训练。在来自14个领域的34个数据集上的大量实验表明，OFA-TAD在严格的OFA设置下实现了优越的异常检测性能和强大的跨领域泛化能力。源代码见：https://this URL。
英文摘要：
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. The source code is available at https://github.com/Shiy-Li/OFA-TAD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
