Stereotype Deepening for Anomaly Detection

Yashi Zhou, Qian Zheng, YiBo Yong

Abstract: At present, many anomaly detection researches focus on two problems: one is that the anomaly on pixels cannot be accurately located; the other is that the training data cannot include the anomalies. We introduce the "Stereotype Deepening" algorithm to solve the challenging problems, which uses transitive learning in the process of training the tree-like teacher-student network structure to deepen the "Stereotype". Therefore, in the abnormal area, the descriptors given by the student will deviate from the descriptors given by the teacher. Additionally, peer bias is also taken into account as an abnormal score item. Experiments have been conducted on different types of datasets to prove the effectiveness of this algorithm for anomaly detection and anomaly localization. By comparison, the method proposed in this paper has significant advantages in textures data type.

Keywords: Stereotype deepening, Transitive learning, Knowledge distillation, Anomaly detection, Anomaly localization