Shengxin Lai’s research while affiliated with Solvay (Shanghai) Co., Ltd. and other places

The unsupervised anomaly localization task is challenging due to the absence of abnormal samples during the training phase, dealing with multiple exceptions for the same object, and detecting unseen anomalies. In order to address these problems, we propose a novel approach that consists of a separate teacher-student feature imitation network and a multi-scale processing strategy that combines an image and feature pyramid. Additionally, we design a side task to optimize weight for each student network block through gradient descent algorithm. Compared with these anomaly localization methods based on feature modeling, experimental results demonstrate that our proposed method has a better performance on MVTec dataset which is a real industrial product detection dataset. Furthermore, our multi-scale strategy effectively improves the performance compared to the benchmark method.