# Why Does Counterintuitive Likelihood Behavior Rarely Emerge in Tabular Anomaly Detection?

DevFeed: [Why Does Counterintuitive Likelihood Behavior Rarely Emerge in Tabular Anomaly Detection?](<https://devfeed.tech/articles/why-does-counterintuitive-likelihood-behavior-rarely-emerge-in-tabular-anomaly-detection-99392.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2602.09593>)

Author: Donghwan Kim, Junghun Phee, Hyunsoo Yoon

Published: 2026-10-26T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>), [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Structured-data](<https://devfeed.tech/topics/structured-data.md>), [AIOps for Kubernetes](<https://devfeed.tech/topics/aiops-for-kubernetes.md>)

Tags: [anomaly-detection](<https://devfeed.tech/tags/anomaly-detection.md>), [behavior](<https://devfeed.tech/tags/behavior.md>), [correlation](<https://devfeed.tech/tags/correlation.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [cv](<https://devfeed.tech/tags/cv.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [experiments](<https://devfeed.tech/tags/experiments.md>), [feature](<https://devfeed.tech/tags/feature.md>), [flow](<https://devfeed.tech/tags/flow.md>), [general](<https://devfeed.tech/tags/general.md>), [generative](<https://devfeed.tech/tags/generative.md>), [generative-models](<https://devfeed.tech/tags/generative-models.md>), [image](<https://devfeed.tech/tags/image.md>), [models](<https://devfeed.tech/tags/models.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [performance](<https://devfeed.tech/tags/performance.md>), [practical](<https://devfeed.tech/tags/practical.md>), [tabular-data](<https://devfeed.tech/tags/tabular-data.md>), [theoretical](<https://devfeed.tech/tags/theoretical.md>)

## AI overview

The abstract reports that normalizing-flow likelihood scoring rarely assigns anomalously high likelihoods to anomalies in tabular data, unlike the frequently observed pattern in images. The authors define a domain-agnostic formulation for detecting and evaluating this behavior, then test 47 tabular datasets against 13 baselines and 10 CV/NLP embedding datasets from ADBench. They find the behavior consistently rare in general tabular data and report competitive anomaly-detection performance from normalizing-flow likelihood. Their theoretical and empirical analyses attribute the difference from images to dimensionality and feature correlation.

## Source excerpt

arXiv:2602.09593v2 Announce Type: replace 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.