Anomaly Detection On Unlabeled Cifar 10 Vs
Metriken
AUROC
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | AUROC |
|---|---|
| input-complexity-and-out-of-distribution | 53.5 |
| ssd-a-unified-framework-for-self-supervised-1 | 89.6 |
| mean-shifted-contrastive-loss-for-anomaly | 90.0 |
| input-complexity-and-out-of-distribution | 58.2 |
| out-of-distribution-detection-without-class | 90.8 |
| csi-novelty-detection-via-contrastive | 89.3 |
| out-of-distribution-detection-without-class | 96.7 |
| out-of-distribution-detection-without-class | 93.3 |
| input-complexity-and-out-of-distribution | 52.6 |
| out-of-distribution-detection-without-class | 90.2 |
| input-complexity-and-out-of-distribution | 73.6 |
| classification-based-anomaly-detection-for-1 | 89.2 |
| multi-task-transformation-learning-for-robust | 82.92 |