Papers › Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation
Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation
Jihun Yi, Sungroh Yoon
In this paper, we address the problem of image anomaly detection and segmentation. Anomaly detection involves making a binary decision as to whether an input image contains an anomaly, and anomaly segmentation aims to locate the anomaly on the pixel level. Support vector data description (SVDD) is a long-standing algorithm used for an anomaly detection, and we extend its deep learning variant to the patch-based method using self-supervised learning. This extension enables anomaly segmentation and improves detection performance. As a result, anomaly detection and segmentation performances measured in AUROC on MVTec AD dataset increased by 9.8% and 7.0%, respectively, compared to the previous state-of-the-art methods. Our results indicate the efficacy of the proposed method and its potential for industrial application. Detailed analysis of the proposed method offers insights regarding its behavior, and the code is available online.
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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 | BTAD | PatchSVDD | Segmentation AUROC | 93.1 | #14 of 15 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | Patch-SVDD | Detection AUROC | 92.1 | #106 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | Patch-SVDD | FPS | 2.1 | #106 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | Patch-SVDD | Segmentation AUROC | 95.7 | #106 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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