調整先驗數據擬合網絡（PFN）以進行表格數據異常偵測
Adapting prior-data fitted networks for tabular anomaly detection
研究利用先驗數據擬合網絡（PFN）的表示進行表格數據異常偵測，提出無需微調的 ZEN 及微調方法 FOCUS。在 ADBench 基準測試中，ZEN 的平均 AUROC 高於所有基線，FOCUS 則進一步提升表現。這些方法亦可泛化至不同 PFN 模型。
Published on Oct 5
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Abstract
While deep features have transformed anomaly detection in images and video, their impact on tabular data has been less substantial, partly due to the limited availability of strong deep representations. Recently, prior-data fitted networks (PFNs) have emerged as a promising source of such representations for tabular data. In this work, we investigate how PFN representations can be adapted and leveraged for anomaly detection. The question is harder than it looks. No anomalies are available before deploy- ment, so model parameters cannot be tuned with supervision, and the reference set that defines normal behavior may itself contain the very anomalies it is supposed to reveal. We begin our study using frozen TabPFN features. Scoring each sam- ple by its distance to its nearest neighbors in feature space already gives strong results. We identify which layers to use and a feature-extraction procedure suited to the task. Next, to further improve performance, we use the reference set to fine- tune the model, so that the resulting features better separate normal samples from anomalies. On the ADBench benchmark, our fine-tuning free approach (ZEN) reaches a higher mean AUROC than every baseline, and our fine-tuned method (FOCUS) improves on it further. Our approach also generalizes across PFN models.
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來源：HuggingFace Daily Papers（社區熱門論文） · huggingface.co