Domain Adaptation On Svhn To Mnist
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | Accuracy |
|---|---|
| adversarial-discriminative-domain-adaptation | 80.1 |
| cycada-cycle-consistent-adversarial-domain | 90.4 |
| self-ensembling-for-visual-domain-adaptation | 99.18 |
| discriminative-feature-alignment | 98.9 |
| discriminative-feature-alignment | 98.2 |
| conditional-adversarial-domain-adaptation | 89.2 |
| light-weight-calibrator-a-separable-component | 97.5 |
| from-source-to-target-and-back-symmetric-bi | 76.1 |
| unsupervised-domain-adaptation-using-feature-2 | 98.76 |
| do-we-really-need-to-access-the-source-data | 98.9 |
| maximum-classifier-discrepancy-for | 95.8 |
| fact-federated-adversarial-cross-training | 90.6 |
| learning-semantic-representations-for | 93.3 |
| progressive-feature-alignment-for | 93.9 |