零样本检测表格异常，无需标注数据即可快速部署。
From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection
- 用合成数据预训练，通过上下文学习实现零样本推理。
- 在超过1500个数据集上表现领先，推理速度极快。
- 适合无标注数据的实时异常检测场景，即插即用。
异常检测（OD）广泛应用，但新任务部署常因缺乏标注异常样本而困难，导致算法与超参数选择极为棘手。基础模型（FMs）已革新机器学习，OD也不例外：Shen等（2025）提出首个OD基础模型FoMo-0D，性能显著优于众多基线。本文提出OUTFORMER，基于FoMo-0D改进，引入（1）合成先验混合机制与（2）自进化课程训练策略。OUTFORMER仅在合成标注数据集上预训练，通过将训练数据作为上下文输入，即可零样本推断新任务测试标签。推理仅需前向传播，无需标注异常样本，且不额外训练模型或选型，真正实现即插即用。在主流AdBench及本文提出的两个新大规模基准上表现卓越，涵盖超过1500个数据集，同时保持高速推理。
原文摘要 · Abstract (English)
Outlier detection (OD) is widely used in practice; but its effective deployment on new tasks is hindered by lack of labeled outliers, which makes algorithm and hyperparameter selection notoriously hard. Foundation models (FMs) have transformed ML, and OD is no exception: Shen et. al. (2025) introduced FoMo-0D, the first FM for OD, achieving remarkable performance against numerous baselines. This work introduces OUTFORMER, which advances FoMo-0D with (1) a mixture of synthetic priors and (2) self-evolving curriculum training. OUTFORMER is pretrained solely on synthetic labeled datasets and infers test labels of a new task by using its training data as in-context input. Inference is fast and zero-shot, requiring merely forward pass and no labeled outliers. Thanks to in-context learning, it requires zero additional work-no OD model training or bespoke model selection-enabling truly plug-and-play deployment. OUTFORMER achieves state-of-the-art performance on the prominent AdBench, as well as two new large-scale OD benchmarks that we introduce, comprising over 1,500 datasets, while maintaining speedy inference.
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