Knowledge Distillation Anomaly Detection with Multi-Scale Feature Fusion

Abstract

To enhance the generalization of anomaly detection, this paper proposes a multi-scale detection method based on knowledge distillation. During training, the well-pretrained teacher network is used to teach the student network to learn the feature of normal samples. During testing, the teacher network can still represent anomaly well due to its strong generalization, while the student network cannot. The difference between them makes the detection task available. Furthermore, a mid-level feature pyramid structure is adopted to enhance the ability for handling the anomaly with different size, and a feature reconstruction modular is also employed to enlarge the difference between teacher and student network for an anomaly. The method achieves 97.8% and 97.7% AUC score on pixel and image level respectively, evaluated on the public benchmark-MVTecAD.