Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection

• URL: http://arxiv.org/abs/2103.04257v1
• Date: Sun, 7 Mar 2021 04:25:04 GMT
• Title: Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection
• Authors: Guodong Wang, Shumin Han, Errui Ding, Di Huang
• Abstract summary: Anomaly detection is a challenging task and usually formulated as an unsupervised 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. Very competitive results are delivered on three major benchmarks, significantly superior to the state of the art ones.
• Score: 38.523251117024984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anomaly detection is a challenging task and usually formulated as an unsupervised 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 alignment 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 three major benchmarks, significantly superior to the state of the art ones. In addition, it makes inferences at a very high speed (with 100 FPS for images of the size at 256x256), at least dozens of times faster than the latest counterparts.

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