Domain Adaptation On Visda2017
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Accuracy |
|---|---|
| conditional-adversarial-domain-adaptation | 73.7 |
| confidence-regularized-self-training | 78.1 |
| contrastive-adaptation-network-for | 87.2 |
| reusing-the-task-specific-classifier-as-a | 83.7 |
| combining-inherent-knowledge-of-vision | 92.7 |
| patch-mix-transformer-for-unsupervised-domain | 88.8 |
| fixbi-bridging-domain-spaces-for-unsupervised | 87.2 |
| sentry-selective-entropy-optimization-via | 76.7 |
| deep-transfer-learning-with-joint-adaptation | 58.3 |
| sf-da-2-source-free-domain-adaptation-through | 89.6 |
| source-free-domain-adaptation-via-avatar | 86.0 |
| self-ensembling-for-visual-domain-adaptation | 85.4 |
| deepjdot-deep-joint-distribution-optimal | 66.9 |
| contrastive-vicinal-space-for-unsupervised | 88.5 |
| visual-prompt-tuning-for-test-time-domain | 90.7 |
| cdtrans-cross-domain-transformer-for | 88.4 |
| sf-da-2-source-free-domain-adaptation-through | 88.1 |
| d-sne-domain-adaptation-using-stochastic | 86.15 |
| mic-masked-image-consistency-for-context | 92.8 |
| empowering-source-free-domain-adaptation-with | 93.2 |
| unsupervised-domain-adaption-harnessing | 91.8 |
| feature-fusion-transferability-aware | 93.8 |
| drop-to-adapt-learning-discriminative | 81.5 |
| do-we-really-need-to-access-the-source-data | 82.9 |
| a-closer-look-at-smoothness-in-domain-1 | 89.8 |
| sliced-wasserstein-discrepancy-for | 76.4 |
| unsupervised-domain-adaptation-an-adaptive | 76.1 |
| confidence-regularized-self-training | 78.1 |