Transferability Estimation on Cars
0.693Weighted Kendall's tauNLEEP
Evaluation results
| Method | Links | ||
|---|---|---|---|
| NLEEPModel type=ViT models2022.07 | 0.693 | 1,100 | |
| LogMEModel type=ViT models2022.07 | 0.642 | 11.2 | |
| SFDAModel type=ViT models2022.07 | 0.632 | 59.1 | |
| LogMEWall-Clock Time (s)=18.3, Backbone=11 Supervised CNN models, Pre-trained=ImageNet2022.07 | 0.576 | — | |
| SFDAWall-Clock Time (s)=553.42022.07 | 0.515 | — | |
| LogME2022.07 | 0.506 | — | |
| SFDAWall-Clock Time (s)=274.6, Backbone=11 Supervised CNN models, Pre-trained=ImageNet2022.07 | 0.487 | — | |
| NLEEPWall-Clock Time (s)=16002022.07 | 0.486 | — | |
| SFDA2022.07 | 0.45 | — | |
| LogMEWall-Clock Time (s)=72.72022.07 | 0.375 | — | |
| LEEPWall-Clock Time (s)=8.3, Backbone=11 Supervised CNN models, Pre-trained=ImageNet2022.07 | 0.367 | — | |
| NLEEPWall-Clock Time (s)=973.8, Backbone=11 Supervised CNN models, Pre-trained=ImageNet2022.07 | 0.265 | — | |
| NLEEP2022.07 | 0.099 | — |