Image Classification on CIFAR-10 (test) (Top-1 Accuracy and Rem. Metric)
93.68Top-1 AccuracyDense
Evaluation results
| Method | Links | ||
|---|---|---|---|
| DenseModel=MobileNet-V22024.04 | 93.68 | — | |
| NEPENTHEModel=MobileNet-V22024.04 | 93.26 | — | |
| NEPENTHEModel=ResNet-182024.04 | 92.55 | — | |
| IMPModel=MobileNet-V22024.04 | 92.5 | — | |
| NEPENTHEModel=Swin-T2024.04 | 92.29 | — | |
| EGPModel=MobileNet-V22024.04 | 92.22 | — | |
| EGPModel=ResNet-182024.04 | 92.18 | — | |
| Group lassoModel=ResNet-182024.04 | 92.11 | — | |
| EGPModel=Swin-T2024.04 | 92.01 | — | |
| HrankModel=Swin-T2024.04 | 91.87 | — | |
| HrankModel=MobileNet-V22024.04 | 91.73 | — | |
| HrankModel=ResNet-182024.04 | 91.7 | — | |
| Group lassoModel=Swin-T2024.04 | 91.68 | — | |
| DenseModel=ResNet-182024.04 | 91.66 | — | |
| IMPModel=ResNet-182024.04 | 91.66 | — | |
| DenseModel=Swin-T2024.04 | 91.54 | — | |
| IMPModel=Swin-T2024.04 | 90.53 | — | |
| Smallest weightsModel=Swin-T2024.04 | 89.22 | — | |
| Smallest gradientsModel=Swin-T2024.04 | 89.21 | — | |
| Group lassoModel=MobileNet-V22024.04 | 83 | — | |
| Smallest weightsModel=ResNet-182024.04 | 10 | — | |
| Smallest weightsModel=MobileNet-V22024.04 | 10 | — | |
| Smallest gradientsModel=MobileNet-V22024.04 | 10 | — | |
| Smallest gradientsModel=ResNet-182024.04 | 9.29 | — |