A new method for detecting anomalies by modeling their causes
A Principled Approach to Unsupervised Anomaly Detection
Computer Vision and Pattern Recognition
Summary
Detecting anomalies usually means spotting anything unusual, but often it’s important to understand what caused those unusual things. The authors propose a new way to detect anomalies by guessing the hidden changes that made the data strange, using probabilities. Their method can explain how bad changes happened, not just if something is wrong. This approach improved detection accuracy in industrial images and brain MRIs, while also estimating how the abnormalities appeared.
What this means in practice
- •For industrial inspection teams: Improve quality control by identifying and explaining defects in manufacturing images more accurately using a probabilistic model of anomalies.
- •For medical imaging analysts: Detect brain pathologies in MRI scans while estimating their intensity and shape using a principled approach to anomaly detection.
Authors
James Myles, Matthew Baugh, Johanna P. Müller, Bernhard Kainz, Yingzhen Li
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.