Multi-scale Feature Imitation for Unsupervised Anomaly Localization
This addresses anomaly detection in industrial settings without labeled anomaly data, but it is incremental as it builds on existing feature imitation approaches.
The paper tackled unsupervised anomaly localization by proposing a teacher-student feature imitation network with multi-scale processing, achieving better performance than feature modeling methods on a real industrial product detection dataset.
The unsupervised anomaly localization task faces the challenge of missing anomaly sample training, detecting multiple types of anomalies, and dealing with the proportion of the area of multiple anomalies. A separate teacher-student feature imitation network structure and a multi-scale processing strategy combining an image and feature pyramid are proposed to solve these problems. A network module importance search method based on gradient descent optimization is proposed to simplify the network structure. The experimental results show that the proposed algorithm performs better than the feature modeling anomaly localization method on the real industrial product detection dataset in the same period. The multi-scale strategy can effectively improve the effect compared with the benchmark method.