A Principled Approach to Unsupervised Anomaly Detection

The paper proposes a Bayesian inverse problem formulation for unsupervised anomaly detection, aiming to infer the most probable corruption causing each observation. This approach yields a probabilistic anomaly score based on the energy of inferred corruption parameters and provides a unified framework that recovers several existing methods as special cases. Experiments demonstrate improved object-class AUROC on the MVTec AD dataset and strong detection performance on a brain MRI benchmark, with additional estimates of pathology intensity, bias, and geometry.

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