Single Source Domain Generalization On Digits
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Model Name | Accuracy | Paper Title | Repository |
|---|---|---|---|
| CADA (LeNet) | 80.56 | Center-aware Adversarial Augmentation for Single Domain Generalization | - |
| L2D (LeNet) | 74.46 | Learning to Diversify for Single Domain Generalization | |
| Crafting-Shifts(LeNet) | 82.61 | Crafting Distribution Shifts for Validation and Training in Single Source Domain Generalization | |
| ABA (LeNet) | 76.72 | Adversarial Bayesian Augmentation for Single-Source Domain Generalization | |
| MCL (LeNet) | 78.82 | Meta-causal Learning for Single Domain Generalization | - |
| MetaCNN (LeNet) | 78.76 | Meta Convolutional Neural Networks for Single Domain Generalization | - |
| ProRandConv (LeNet) | 81.35 | Progressive Random Convolutions for Single Domain Generalization | - |
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