Papers › Diversity-Measurable Anomaly Detection
Diversity-Measurable Anomaly Detection
Wenrui Liu, Hong Chang, Bingpeng Ma, Shiguang Shan, Xilin Chen
Reconstruction-based anomaly detection models achieve their purpose by suppressing the generalization ability for anomaly. However, diverse normal patterns are consequently not well reconstructed as well. Although some efforts have been made to alleviate this problem by modeling sample diversity, they suffer from shortcut learning due to undesired transmission of abnormal information. In this paper, to better handle the tradeoff problem, we propose Diversity-Measurable Anomaly Detection (DMAD) framework to enhance reconstruction diversity while avoid the undesired generalization on anomalies. To this end, we design Pyramid Deformation Module (PDM), which models diverse normals and measures the severity of anomaly by estimating multi-scale deformation fields from reconstructed reference to original input. Integrated with an information compression module, PDM essentially decouples deformation from prototypical embedding and makes the final anomaly score more reliable. Experimental results on both surveillance videos and industrial images demonstrate the effectiveness of our method. In addition, DMAD works equally well in front of contaminated data and anomaly-like normal samples.
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Code
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Code Syntology ran Syntology
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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 | CUHK Avenue | DMAD | AUC | 92.8% | #8 of 35 | Archive leaderboard | report |
| Anomaly Detection | CUHK Avenue | ConvVQ | AUC | 84.3% | #32 of 35 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | DMAD | Detection AUROC | 99.5 | #30 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | DMAD | Segmentation AUROC | 98.2 | #30 of 148 | Archive leaderboard | report |
| Anomaly Detection | ShanghaiTech | DMAD | AUC | 78.8% | #19 of 31 | Archive leaderboard | report |
| Anomaly Detection | UCSD Ped2 | DMAD | AUC | 99.7% | #1 of 14 | Archive leaderboard | report |
| Anomaly Detection | UCSD Ped2 | ConvVQ | AUC | 90.2% | #14 of 14 | 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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