Student-Teacher Feature Pyramid Matching for Anomaly Detection

TL;DR

This paper introduces a student-teacher feature pyramid matching method for anomaly detection that improves accuracy and efficiency by leveraging multi-scale features and knowledge distillation, achieving state-of-the-art results.

Contribution

It extends the student-teacher framework with hierarchical multi-scale feature matching for enhanced anomaly detection performance.

Findings

Achieves superior accuracy on MVTec dataset.

Provides fast, pixel-level anomaly detection.

Outperforms existing state-of-the-art methods.

Abstract

Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies. This paper proposes a simple yet powerful approach to this issue, which is implemented in the student-teacher framework for its advantages but substantially extends it in terms of both accuracy and efficiency. Given a strong model pre-trained on image classification as the teacher, we distill the knowledge into a single student network with the identical architecture to learn the distribution of anomaly-free images and this one-step transfer preserves the crucial clues as much as possible. Moreover, we integrate the multi-scale feature matching strategy into the framework, and this hierarchical feature matching enables the student network to receive a mixture of multi-level knowledge from the feature pyramid under better supervision, thus allowing to detect anomalies of various sizes. The difference between feature pyramids generated by the two networks serves as a scoring function indicating the probability of anomaly occurring. Due to such operations, our approach achieves accurate and fast pixel-level anomaly detection. Very competitive results are delivered on the MVTec anomaly detection dataset, superior to the state of the art ones.