Unsupervised Domain Adaptation On Office Home
평가 지표
Accuracy
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | Accuracy |
|---|---|
| imagenet-classification-with-deep | 54.9 |
| prompt-based-distribution-alignment-for | 75.3 |
| prompt-based-distribution-alignment-for | 85.7 |
| deep-residual-learning-for-image-recognition | 59.9 |
| unsupervised-domain-adaptation-via-distilled | - |
| transadapter-vision-transformer-for-feature | 89.4 |
| feature-fusion-transferability-aware | 91.4 |
| patch-mix-transformer-for-unsupervised-domain | 89.0 |
| domain-adversarial-training-of-neural | 76.8 |
| learning-transferable-features-with-deep | 74.3 |
| euda-an-efficient-unsupervised-domain | 84.9 |
| deep-transfer-learning-with-joint-adaptation | 76.8 |
| semantic-aware-message-broadcasting-for | 86.20 |
| learning-cnn-on-vit-a-hybrid-model-to | 81.2 |
| chatty-coupled-holistic-adversarial-transport | 73 |
| empowering-source-free-domain-adaptation-with | 90.0 |
| implicit-class-conditioned-domain-alignment | - |
| safe-self-refinement-for-transformer-based | 85.43 |
| cdtrans-cross-domain-transformer-for | 80.5 |