一个能跨数据集和监督模式的表格异常检测模型。
ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes
- 用上下文学习方式，不更新权重直接评分。
- 57个数据集测试，三种监督模式均领先。
- 适合需要统一异常检测方案的场景。
表格数据异常检测通常在三种监督模式下研究：仅含正常样本的一类设置、无标签且可能含异常的全无监督设置，以及仅有少量异常标签的半监督设置。现有深度学习方法通常针对单一监督模式训练特定模型，难以利用不同任务间的共享结构或适应不同监督水平。我们提出ICLAD，一种基于元学习在合成表格异常检测任务上训练的上下文学习基础模型，可在推理时通过条件化训练集生成异常分数，无需更新模型参数。在ADBench的57个表格数据集上的全面实验表明，该方法在三种监督模式下均达到当前最优性能，建立了统一的表格异常检测框架。
原文摘要 · Abstract (English)
Anomaly detection on tabular data is commonly studied under three supervision regimes, including one-class settings that assume access to anomaly-free training samples, fully unsupervised settings with unlabeled and potentially contaminated training data, and semi-supervised settings with limited anomaly labels. Existing deep learning approaches typically train dataset-specific models under the assumption of a single supervision regime, which limits their ability to leverage shared structures across anomaly detection tasks and to adapt to different supervision levels. We propose ICLAD, an in-context learning foundation model for tabular anomaly detection that generalizes across both datasets and supervision regimes. ICLAD is trained via meta-learning on synthetic tabular anomaly detection tasks, and at inference time, the model assigns anomaly scores by conditioning on the training set without updating model weights. Comprehensive experiments on 57 tabular datasets from ADBench show that our method achieves state-of-the-art performance across three supervision regimes, establishing a unified framework for tabular anomaly detection.
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