ROT+Trans

Reported on 2 benchmarks across 1 task · 1 paper · 2 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology2 results

Anomaly DetectiononAnomaly Detection on Anomaly Detection on Unlabeled ImageNet-30 vs Flowers-102
ROC-AUC· 2019-06-28
86.3
best: 98.3 (PsudoLabels CLIP ViT)
SOTA
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty arXiv:1906.12340
Anomaly DetectiononAnomaly Detection on Unlabeled ImageNet-30 vs CUB-200
ROC-AUC· 2019-06-28
74.5
best: 99.4 (PsudoLabels CLIP ViT)
SOTA
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty arXiv:1906.12340