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Unsupervised methods based on density representation have shown their abilities in anomaly detection, but detection performance still needs to be improved. Specifically, approaches using normalizing flows can accurately evaluate sample distributions, mapping normal features to the normal distribution and anomalous features outside it. Consequently, this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network (NF-BMR). It utilizes pre-trained Convolutional Neural Networks (CNN) and normalizing flows to construct discriminative source and target domain feature spaces. Additionally, to better learn feature information in both domain spaces, we propose the Bidirectional Mapping Residual Network (BMR), which maps sample features to these two spaces for anomaly detection. The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives, which leads to the improvement of detection performance. Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network (B-PFM) and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance. On the MVTec AD dataset, NF-BMR achieves an average AUROC of 98.7% for all 15 categories. Especially, it achieves 100% optimal detection performance in five categories. On the DAGM dataset, the average AUROC across ten categories is 98.7%, which is very close to supervised methods.

A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection