Image Classification on Blended-Office-Home-LMT ResNet-50 (test)
99.7Accuracy (Clipart)S+T
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| S+TBackbone=ResNet-50, supervised learning on source and target=true2023.02 | 99.7 | 99.8 | 99.8 | 99.8 | |
| MCDABackbone=ResNet-50, oracle=true2023.02 | 98.9 | 98.3 | 98.2 | 98.5 | |
| MCDABackbone=ResNet-50, data augmentation=RandAug2023.02 | 69.1 | 66.2 | 68.9 | 68.1 | |
| MCDABackbone=ResNet-502023.02 | 68 | 62.3 | 67.5 | 65.9 | |
| SENTRYBackbone=ResNet-50, data augmentation=RandAug2023.02 | 65.6 | 63.5 | 65.9 | 65 | |
| MDDIABackbone=ResNet-502023.02 | 61.9 | 58.2 | 63.2 | 61.1 | |
| CSTBackbone=ResNet-50, data augmentation=RandAug2023.02 | 58.3 | 57.4 | 63.4 | 59.7 | |
| CGCTBackbone=ResNet-50, balanced sampling=true2023.02 | 57.1 | 53 | 56.8 | 55.7 | |
| CGCTBackbone=ResNet-502023.02 | 53.7 | 51.5 | 52 | 52.4 | |
| BSPBackbone=ResNet-502023.02 | 51.5 | 52.9 | 57.4 | 54 | |
| JANBackbone=ResNet-502023.02 | 51.4 | 50.1 | 57 | 53.2 | |
| DANBackbone=ResNet-502023.02 | 51 | 49.2 | 56.8 | 52.3 | |
| CDANBackbone=ResNet-502023.02 | 50.5 | 53.2 | 56.3 | 53.3 | |
| DANNBackbone=ResNet-502023.02 | 46.6 | 50.4 | 53.3 | 50.1 | |
| ADDABackbone=ResNet-502023.02 | 45 | 49.7 | 52.8 | 49.2 | |
| MDDBackbone=ResNet-502023.02 | 43.7 | 56 | 57.8 | 52.5 | |
| SourceBackbone=ResNet-502023.02 | 42.3 | 47.6 | 50.3 | 46.7 | |
| MCDBackbone=ResNet-502023.02 | 40.2 | 48.6 | 52.3 | 47 |