Single Source Domain Generalization On Pacs
Metrics
Accuracy
Results
Performance results of various models on this benchmark
| Paper Title | ||
|---|---|---|
| Crafting-Shifts(ResNet18) | 70.37 | Crafting Distribution Shifts for Validation and Training in Single Source Domain Generalization |
| MCL (ResNet18) | 69.86 | Meta-causal Learning for Single Domain Generalization |
| ProRandConv (ResNet18) | 68.88 | Progressive Random Convolutions for Single Domain Generalization |
| CADA (ResNet18) | 68.41 | Center-aware Adversarial Augmentation for Single Domain Generalization |
| ITTA (ResNet18) | 68.4 | Improved Test-Time Adaptation for Domain Generalization |
| XDED (ResNet18) | 66.5 | Cross-Domain Ensemble Distillation for Domain Generalization |
| ABA (ResNet18) | 66.36 | Adversarial Bayesian Augmentation for Single-Source Domain Generalization |
| GeoTexAug (ResNet18) | 65.0 | Geometric and Textural Augmentation for Domain Gap Reduction |
| SagNet (ResNet18) | 61.9 | Reducing Domain Gap by Reducing Style Bias |
| SelfReg (ResNet18) | 59.59 | SelfReg: Self-supervised Contrastive Regularization for Domain Generalization |
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