TaskBridge：通过虚拟任务桥接无监督表格异常检测与上下文学习
TaskBridge: Bridging Unsupervised Tabular Anomaly Detection and In-Context Learning via Virtual Tasks
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中文总结 AI 辅助
TaskBridge通过构建虚拟监督任务，将无监督表格异常检测重构为上下文学习，无需异常预训练，在790个数据集上超越30个基线。
中文摘要 AI 辅助
无监督表格异常检测（TAD）旨在利用正常训练样本识别表格数据中的异常行。传统方法依赖特定数据集的训练和配置搜索，而近期出现的表格基础模型（TFMs）通过上下文学习能够对未见过的数据集进行零样本异常检测。然而，大多数基于TFM的方法需要从头开始进行针对异常的预训练，这使得检测本质上依赖于合成的TAD特定先验知识，且更新成本高昂。部分方法转而复用预训练的通用TFM进行TAD以避免此负担，但依赖于计算开销大且带有严格异常归纳偏置的公式。在本工作中，我们提出了TaskBridge，一种新框架，通过构建虚拟监督任务，将异常检测直接重构为TFM的监督式上下文推断，从而高效地复用预训练的通用TFM进行无监督TAD。所生成的虚拟任务诱导出预测结构，在此结构下，正常查询及其目标对获得高支持度，而异常则倾向于违反该结构并获得较低支持度，从而提供直接的异常证据。在790个真实世界数据集上，TaskBridge持续优于30个基线方法，包括最先进的基于TFM的方法，且无需针对异常的TFM预训练或特定数据集的模型优化。
英文摘要
Unsupervised tabular anomaly detection (TAD) aims to identify anomalous rows in tabular data using normal training samples. While conventional methods rely on dataset-specific training and configuration search, recent tabular foundation models (TFMs) enable zero-shot anomaly detection on unseen datasets via in-context learning. Most TFM-based approaches, however, require anomaly-specific pretraining from scratch, making detection inherently dependent on synthetic TAD-specific priors and costly to update. Some approaches instead repurpose pretrained general-purpose TFMs for TAD to avoid this burden, but rely on computationally expensive formulations with restrictive anomaly inductive biases. In this work, we introduce TaskBridge, a new framework that efficiently repurposes pretrained general-purpose TFMs for unsupervised TAD by constructing virtual supervised tasks that directly recast anomaly detection as supervised in-context inference of TFMs. The resulting virtual tasks induce predictive structures under which normal queries and their target pairs receive high support, whereas anomalies tend to violate the induced structures and receive lower support, providing direct anomaly evidence. Across 790 real-world datasets, TaskBridge consistently outperforms 30 baselines, including state-of-the-art TFM-based approaches, without anomaly-specific TFM pretraining or dataset-specific model optimization.
发表机构
- Seoul National University（首尔大学）
- KAIST（韩国科学技术院）
- Nums AI Inc.（Nums AI 公司）
机构由 AI 辅助整理，请以论文原文为准。
