Papers › Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization
Hannah M. Schlüter, Jeremy Tan, Benjamin Hou, Bernhard Kainz
We introduce a simple and intuitive self-supervision task, Natural Synthetic Anomalies (NSA), for training an end-to-end model for anomaly detection and localization using only normal training data. NSA integrates Poisson image editing to seamlessly blend scaled patches of various sizes from separate images. This creates a wide range of synthetic anomalies which are more similar to natural sub-image irregularities than previous data-augmentation strategies for self-supervised anomaly detection. We evaluate the proposed method using natural and medical images. Our experiments with the MVTec AD dataset show that a model trained to localize NSA anomalies generalizes well to detecting real-world a priori unknown types of manufacturing defects. Our method achieves an overall detection AUROC of 97.2 outperforming all previous methods that learn without the use of additional datasets. Code available at https://github.com/hmsch/natural-synthetic-anomalies.
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Code
Syntology Ran 2 of 8 code samples harvested from 1 repository linked to this paper; 6 have no recorded run. Of those that ran: 2 ran · our draft was wrong.
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Code Syntology ran Syntology
8 samples harvested; 2 ran; 0 honoured the contract we drafted; 6 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Classification | GoodsAD | NSA | AUPR | 71.8 | #5 of 11 | Archive leaderboard | report |
| Anomaly Classification | GoodsAD | NSA | AUROC | 67.3 | #5 of 11 | Archive leaderboard | report |
| Anomaly Detection | AeBAD-S | NSA | Detection AUROC | 56.7 | #6 of 8 | Archive leaderboard | report |
| Anomaly Detection | AeBAD-S | NSA | Segmentation AUPRO | 45.9 | #6 of 8 | Archive leaderboard | report |
| Anomaly Detection | AeBAD-V | NSA | Detection AUROC | 64.6 | #5 of 7 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | NSA | Detection AUROC | 97.2 | #78 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | NSA | Segmentation AUPRO | 91.0 | #78 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | NSA | Segmentation AUROC | 96.3 | #78 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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