Papers › Reconstructed Student-Teacher and Discriminative Networks for Anomaly Detection
Reconstructed Student-Teacher and Discriminative Networks for Anomaly Detection
Shinji Yamada, Satoshi Kamiya, Kazuhiro Hotta
Anomaly detection is an important problem in computer vision; however, the scarcity of anomalous samples makes this task difficult. Thus, recent anomaly detection methods have used only normal images with no abnormal areas for training. In this work, a powerful anomaly detection method is proposed based on student-teacher feature pyramid matching (STPM), which consists of a student and teacher network. Generative models are another approach to anomaly detection. They reconstruct normal images from an input and compute the difference between the predicted normal and the input. Unfortunately, STPM does not have the ability to generate normal images. To improve the accuracy of STPM, this work uses a student network, as in generative models, to reconstruct normal features. This improves the accuracy; however, the anomaly maps for normal images are not clean because STPM does not use anomaly images for training, which decreases the accuracy of the image-level anomaly detection. To further improve accuracy, a discriminative network trained with pseudo-anomalies from anomaly maps is used in our method, which consists of two pairs of student-teacher networks and a discriminative network. The method displayed high accuracy on the MVTec anomaly detection dataset.
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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 | RSTPM | Detection AUROC | 98.7 | #50 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | RSTPM | Segmentation AUPRO | 95.1 | #50 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | RSTPM | Segmentation AUROC | 98.5 | #50 of 148 | 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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