Papers › Prototypical Residual Networks for Anomaly Detection and Localization
Prototypical Residual Networks for Anomaly Detection and Localization
HUI ZHANG, Zuxuan Wu, Zheng Wang, Zhineng Chen, Yu-Gang Jiang
Anomaly detection and localization are widely used in industrial manufacturing for its efficiency and effectiveness. Anomalies are rare and hard to collect and supervised models easily over-fit to these seen anomalies with a handful of abnormal samples, producing unsatisfactory performance. On the other hand, anomalies are typically subtle, hard to discern, and of various appearance, making it difficult to detect anomalies and let alone locate anomalous regions. To address these issues, we propose a framework called Prototypical Residual Network (PRN), which learns feature residuals of varying scales and sizes between anomalous and normal patterns to accurately reconstruct the segmentation maps of anomalous regions. PRN mainly consists of two parts: multi-scale prototypes that explicitly represent the residual features of anomalies to normal patterns; a multisize self-attention mechanism that enables variable-sized anomalous feature learning. Besides, we present a variety of anomaly generation strategies that consider both seen and unseen appearance variance to enlarge and diversify anomalies. Extensive experiments on the challenging and widely used MVTec AD benchmark show that PRN outperforms current state-of-the-art unsupervised and supervised methods. We further report SOTA results on three additional datasets to demonstrate the effectiveness and generalizability of PRN.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Supervised Anomaly Detection | MVTec AD | PRN | Detection AUROC | 99.4 | #4 of 8 | Archive leaderboard | report |
| Supervised Anomaly Detection | MVTec AD | PRN | Segmentation AP | 78.6 | #4 of 8 | Archive leaderboard | report |
| Supervised Anomaly Detection | MVTec AD | PRN | Segmentation AUPRO | 96.1 | #4 of 8 | Archive leaderboard | report |
| Supervised Anomaly Detection | MVTec AD | PRN | Segmentation AUROC | 99.0 | #4 of 8 | 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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