MAPL: Memory Augmentation and Pseudo-Labeling for Semi-Supervised Anomaly Detection

Large unlabeled data and difficult-to-identify anomalies are the urgent issues need to overcome in most industrial scene. In order to address this issue, a new methodology for detecting surface defects in industrial settings is introduced, referred to as Memory Augmentation and Pseudo-Labeling MAPL . The methodology first introduces an anomaly simulation strategy, which significantly improves the model's ability to recognize rare or unknown anomaly types by generating simulated anomaly samples. To cope with the problem of the lack of labeling of anomalous simulated samples, a pseudo-labeler method based on a one-classifier ensemble was employed in this study, which enhances the robustness of the model in the case of limited labeling data by automatically selecting key pseudo-labeling hyperparameters. Meanwhile, a memory-enhanced learning mechanism is introduced to effectively predict abnormal regions by analyzing the difference between the input samples and the normal samples in the memory pool. An end-to-end learning framework is employed by MAPL to identify the abnormal regions directly from the input data, which optimizes the efficiency and real-time performance of detection. By conducting extensive trials on the recently developed BHAD dataset including MVTec AD [1], Visa [2], and MDPP [3] , MAPL achieves an average image-level AUROC score of 86.2 , demonstrating a 5.1 enhancement compared to the original MemSeg [4] model. The source code is available at https: github.com jzc777 MAPL.