arXiv cs.AI· Donghwan Kim, Junghun Phee, Hyunsoo Yoon·· 12 小时前AI 评分31
为何表格异常检测中反直觉似然行为很少出现？
Why Does Counterintuitive Likelihood Behavior Rarely Emerge in Tabular Anomaly Detection?
AI 导读
研究发现，深度生成模型在图像领域常给异常数据更高似然的反直觉现象，在表格数据中很少出现。作者提出领域无关的形式化定义，在 47 个表格数据集、13 个基线模型及 ADBench 的 10 个 CV/NLP 嵌入数据集上验证该现象持续罕见，normalizing flow 的似然检测性能具竞争力。理论分析将差异归因于表格与图像数据在维度和特征相关性上的不同。
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