PGTNet: Prototype Guided Transfer Network for Few-Shot Anomaly Localization

Zisong Zhuang, Junhang Zhang, Luwei Xiao, Tianlong Ma, Liang He

Abstract: Anomaly localization is pixel-level regions detection in the image. The challenge is how to generate accurate representations of the novel anomaly types which are multifarious. Besides, the anomaly sample size is often not enough to support model learning to detection because of the limitations of real conditions. In this work, we present a novel few-shot setting for anomaly detection and reorganize the defective datasets. Based on the few-shot learning, we transfer the idea of metric learning and propose the prototype-guided transfer network (PGTNet). Extensive experiment results suggest that PGT-Net outperforms current SOTA methods and provides a novel perspective for the anomaly localization task.