Domain Generalization On Imagenet C
المقاييس
Top 1 Accuracy
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
جدول المقارنة
| اسم النموذج | Top 1 Accuracy |
|---|---|
| when-vision-transformers-outperform-resnets | 48.9 |
| amplitude-phase-recombination-rethinking | - |
| metaformer-baselines-for-vision | - |
| metaformer-baselines-for-vision | - |
| understanding-the-robustness-in-vision | 67.7 |
| global-filter-networks-for-image | - |
| masked-autoencoders-are-scalable-vision | - |
| pushpull-net-inhibition-driven-resnet-robust | 69.4 |
| a-convnet-for-the-2020s | - |
| fully-attentional-networks-with-self-emerging-1 | 69.2 |
| metaformer-baselines-for-vision | - |
| quality-agnostic-image-recognition-via | - |
| amplitude-phase-recombination-rethinking | - |
| dinov2-learning-robust-visual-features | - |
| discrete-representations-strengthen-vision-1 | - |
| diffusion-based-adaptation-for-classification | 61 |
| augmix-a-simple-data-processing-method-to | - |
| dinov2-learning-robust-visual-features | - |
| diffusion-based-adaptation-for-classification | 52.1 |
| generalized-parametric-contrastive-learning | - |
| dinov2-learning-robust-visual-features | - |
| discrete-representations-strengthen-vision-1 | - |
| understanding-the-robustness-in-vision | 70.5 |
| enhance-the-visual-representation-via | - |
| improving-vision-transformers-by-revisiting | - |
| dinov2-learning-robust-visual-features | - |
| rethinking-the-design-principles-of-robust | - |
| prime-a-few-primitives-can-boost-robustness | 55.0 |
| rethinking-the-design-principles-of-robust | - |
| quality-agnostic-image-recognition-via | - |
| diffusion-based-adaptation-for-classification | 64.3 |
| when-vision-transformers-outperform-resnets | 55 |
| prime-a-few-primitives-can-boost-robustness | 56.4 |
| metaformer-baselines-for-vision | - |
| group-wise-inhibition-based-feature | - |
| the-many-faces-of-robustness-a-critical | - |
| imagenet-trained-cnns-are-biased-towards | - |
| sequencer-deep-lstm-for-image-classification | - |
| discrete-representations-strengthen-vision-1 | - |
| when-vision-transformers-outperform-resnets | 56.5 |
| understanding-the-robustness-in-vision | 73.6 |
| prime-a-few-primitives-can-boost-robustness | 59.9 |
| pyramid-adversarial-training-improves-vit | - |
| metaformer-baselines-for-vision | - |
| benchmarking-neural-network-robustness-to-2 | - |
| rethinking-the-design-principles-of-robust | - |
| pyramid-adversarial-training-improves-vit | - |