为什么反直觉的似然行为在表格异常检测中很少出现？
Why Does Counterintuitive Likelihood Behavior Rarely Emerge in Tabular Anomaly Detection?
AI 导读
该研究以 normalizing flow 为代表、基于可 tractable 似然的深度生成模型异常检测，指出图像域中常见的“异常数据获得更高似然”这一反直觉现象在表格数据中很少出现。
正文 · 原文
Abstract:Deep generative models with tractable likelihoods, exemplified by normalizing flows, offer an effective basis for anomaly detection through likelihood-based scoring. We demonstrate that, unlike in the image domain where deep generative models frequently assign higher likelihoods to anomalous data, such counterintuitive behavior occurs rarely in tabular settings. We first introduce a domain-agnostic formulation that enables consistent detection and evaluation of the counterintuitive phenomenon, addressing the lack of a precise definition in prior work. Through extensive experiments on 47 tabular datasets benchmarked against 13 baseline models and on 10 CV/NLP embedding datasets from ADBench, we show that the phenomenon is consistently rare in general tabular data and that normalizing flow likelihood achieves competitive anomaly detection performance. We further provide theoretical and empirical analyses attributing this disparity to differences in dimensionality and feature correlation between tabular and image data. Therefore, our results indicate that likelihood-based detection with normalizing flows is a practical approach for tabular anomaly detection.
| Comments: | 53 pages, 11 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2602.09593 [cs.LG] |
| (or arXiv:2602.09593v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2602.09593 arXiv-issued DOI via DataCite |
Submission history
From: Donghwan Kim [view email]
[v1]
Tue, 10 Feb 2026 09:47:34 UTC (910 KB)
[v2]
Fri, 23 Oct 2026 06:39:06 UTC (905 KB)
来源：arXiv cs.AI · arxiv.org