Unsupervised Anomaly Detection with an Enhanced Teacher for Student-Teacher Feature Pyramid Matching

Anomaly detection or outlier is one of the challenging subjects in unsupervised learning. This paper is introduced a student-teacher framework for anomaly detection that its teacher network is enhanced for achieving high-performance metrics. For this purpose, we first pretrain the ResNet-18 network on the ImageNet and then finetune it on the MVTech-AD dataset. Experiment results on the image-level and pixel-level demonstrate that this idea has achieved better metrics than the previous methods. Our model, Enhanced Teacher for Student-Teacher Feature Pyramid (ET-STPM), achieved 0.971 mean accuracy on the image-level and 0.977 mean accuracy on the pixel-level for anomaly detection.