Papers › Student-Teacher Feature Pyramid Matching for Anomaly Detection
Student-Teacher Feature Pyramid Matching for Anomaly Detection
Guodong Wang, Shumin Han, Errui Ding, Di Huang
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Detection | MVTec AD | STPM | Detection AUROC | 95.5 | #88 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | STPM | Segmentation AUROC | 97.0 | #88 of 148 | Archive leaderboard | report |
| Anomaly Detection | VisA | STPM | Detection AUROC | 83.3 | #37 of 50 | Archive leaderboard | report |
| Anomaly Detection | VisA | STPM | Segmentation AUPRO (until 30% FPR) | 62.0 | #37 of 50 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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