Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework

Surrogate task based methods have recently shown great promise for unsupervised image anomaly detection. However, there is no guarantee that the surrogate tasks share the consistent optimization direction with anomaly detection. In this paper, we return to a direct objective function for anomaly detection with information theory, which maximizes the distance between normal and anomalous data in terms of the joint distribution of images and their representation. To make this objective function directly optimizable under the unsupervised setting, we manage to find its lower bound which weights the trade-off between mutual information and entropy, which leads to a novel information theoretic framework for unsupervised image anomaly detection. Extensive experiments on several benchmark data sets have shown that the proposed framework significantly outperforms several state-of-the-arts.