Unsupervised Domain Adaptation On Imagenet R
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Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
| Paper Title | ||
|---|---|---|
| ResNet50, BatchNorm adaptation | 59.9 | Improving robustness against common corruptions by covariate shift adaptation |
| ResNet50 + ENT | 56.1 | If your data distribution shifts, use self-learning |
| ResNet50 + RPL | 54.1 | If your data distribution shifts, use self-learning |
| ResNet50+DeepAug+Augmix, BatchNorm adaptation | 48.9 | Improving robustness against common corruptions by covariate shift adaptation |
| ResNeXt101+DeepAug+AugMix, BatchNorm Adaptation, | 44.0 | Improving robustness against common corruptions by covariate shift adaptation |
| EfficientNet-L2 Noisy Student + ENT | 19.7 | If your data distribution shifts, use self-learning |
| EfficientNet-L2 Noisy Student + RPL | 17.4 | If your data distribution shifts, use self-learning |
| Model soups (ViT-G/14) | 4.54 | Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time |
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