A Principled Approach to Unsupervised Anomaly Detection

2026-09-18

AI Summary

该研究通过将无监督异常检测重新定义为贝叶斯逆问题，推断每个观测值最可能的破坏源，从而改进了现有方法，并在实验中提高了异常检测性能。

Abstract

Traditional unsupervised anomaly detection (UAD) methods are designed to flag or localise deviations from a normative distribution, ignoring the underlying generative mechanisms of the anomalies. Yet the nature of an anomaly is often as important as its presence. We reformulate UAD as a Bayesian inverse problem, in which the objective is to infer the most probable corruption responsible for each observation. Our framework yields a probabilistic anomaly score as the energy of the inferred corruption parameters, and serves as a principled recipe for developing new UAD algorithms. We derive several existing methods as instances of the general framework, each corresponding to the same energy score under different modelling choices. Experimentally, we study the framework's components in a controlled setting, and improve object-class AUROC on the MVTec AD dataset by 2.3% by adapting the underlying corruption model. Finally, we validate the framework on a brain MRI benchmark, achieving strong detection performance while producing estimates of pathology intensity, bias, and geometry. Code is available at https://github.com/jgmyles/inverse-uad.

Problem

• unsupervised anomaly detection
• generative mechanisms
• anomaly nature

Innovation

• Bayesian inverse problem
• probabilistic anomaly score
• corruption model
• principled UAD framework