Papers › Classification-Based Anomaly Detection for General Data
Classification-Based Anomaly Detection for General Data
Liron Bergman, Yedid Hoshen
Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence. Recently, classification-based methods were shown to achieve superior results on this task. In this work, we present a unifying view and propose an open-set method, GOAD, to relax current generalization assumptions. Furthermore, we extend the applicability of transformation-based methods to non-image data using random affine transformations. Our method is shown to obtain state-of-the-art accuracy and is applicable to broad data types. The strong performance of our method is extensively validated on multiple datasets from different domains.
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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 | Anomaly Detection on Anomaly Detection on Unlabeled ImageNet-30 vs Flowers-102 | GOAD | Network | ResNet-18 | #4 of 5 | Archive leaderboard | report |
| Anomaly Detection | Anomaly Detection on Anomaly Detection on Unlabeled ImageNet-30 vs Flowers-102 | GOAD | ROC-AUC | 92.8 | #4 of 5 | Archive leaderboard | report |
| Anomaly Detection | Anomaly Detection on Unlabeled CIFAR-10 vs LSUN (Fix) | GOAD | Network | ResNet-18 | #8 of 8 | Archive leaderboard | report |
| Anomaly Detection | Anomaly Detection on Unlabeled CIFAR-10 vs LSUN (Fix) | GOAD | ROC-AUC | 78.8 | #8 of 8 | Archive leaderboard | report |
| Anomaly Detection | Anomaly Detection on Unlabeled ImageNet-30 vs CUB-200 | GOAD | Network | ResNet-18 | #3 of 5 | Archive leaderboard | report |
| Anomaly Detection | Anomaly Detection on Unlabeled ImageNet-30 vs CUB-200 | GOAD | ROC-AUC | 90.5 | #3 of 5 | Archive leaderboard | report |
| Anomaly Detection | One-class CIFAR-10 | GOAD | AUROC | 88.2 | #22 of 36 | Archive leaderboard | report |
| Anomaly Detection | UEA time-series datasets | GOAD | Avg. ROC-AUC | 87.2 | #2 of 3 | Archive leaderboard | report |
| Anomaly Detection | Unlabeled CIFAR-10 vs CIFAR-100 | GOAD | AUROC | 89.2 | #8 of 13 | Archive leaderboard | report |
| Anomaly Detection | Unlabeled CIFAR-10 vs CIFAR-100 | GOAD | Network | ResNet-18 | #8 of 13 | 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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