| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Anomaly Detection | ADBench noisy-context scenario 1.0 | F1 Score84.78 | 532 | |
| Anomaly Detection | ADBench Tabular (aggregated across 47 datasets) | Average AUROC86.0246 | 41 | |
| Anomaly Detection | ADBench | Mean AUCROC90.43 | 34 | |
| Anomaly Detection | ADBench MNIST-C | F1 Score21.86 | 26 | |
| Anomaly Detection | ADBench breastw | F1 Score94.15 | 26 | |
| Anomaly Detection | ADBench aloi | Average AUC PR10.36 | 26 | |
| Outlier Detection | ADBench v1 (train) | ALOI0.147 | 19 | |
| Anomaly Detection | ADBench 57 Datasets (test) | AP54.7 | 19 | |
| Anomaly Detection | ADBench ID 2 | AUCROC92.68 | 17 | |
| Anomaly Detection | ADBench ID 1 | AUCROC66.95 | 17 | |
| Anomaly Detection | ADBench anomaly ratio categories | AP (< 3%)31.3 | 15 | |
| Anomaly Detection | ADBench unsupervised (contaminated) setting | AUC-PR34.3 | 15 | |
| Anomaly Detection | ADBench 38 real-world datasets (clean-context) | F1 Score65.52 | 14 | |
| Anomaly Detection | ADBench All Datasets 57 datasets | ROC-AUC81.49 | 10 | |
| Anomaly Detection | ADBench Low Dimensionality, d ≤ 20 (24 datasets) | ROC-AUC90.14 | 10 | |
| Anomaly Detection | ADBench Medium Dimensionality (20 < d ≤ 100) | ROC-AUC80.13 | 10 | |
| Anomaly Detection | ADBench High Dimensionality d > 100 (15 datasets) | ROC-AUC73.98 | 10 | |
| Semi-supervised Anomaly Detection | ADBench 100% labeled anomaly ratio | Mean Rank (Rk)2.2 | 9 | |
| Semi-supervised Anomaly Detection | ADBench 25% labeled anomaly ratio | Mean Rank (Rk)2.5 | 9 | |
| Semi-supervised Anomaly Detection | ADBench 5% labeled anomaly ratio | Mean Rank2.9 | 9 | |
| Semi-supervised Anomaly Detection | ADBench 1% labeled anomaly ratio | Mean Rank2.7 | 9 | |
| Semi-supervised Novelty Detection | ADBench Novelty | Mean Rank2.6 | 9 | |
| Anomaly Detection | ADBench One-Class | Mean Rank3.3 | 9 | |
| Anomaly Detection | ADBench Unsupervised | Mean Rank3.9 | 9 | |
| Anomaly Detection | ADBench annthyroid | Average AUC PR82.88 | 9 |