Table 1.
Comparison of Top-1 accuracy on CIFAR-10 and CIFAR-100 datasets with various baseline models and attention based classification models.
| CIFAR-100 | CIFAR-10 | |
|---|---|---|
| Model | Top- 1 | Top-1 |
| ResNet-110 | 73.12 | 93.57 |
| ResNet-110-SE | 76.15 | 94.79 |
| ResNet-110-SAOL | 77.15 | 95.18 |
| ResNet-110-ABN | 77.15 | 95.09 |
| ResNet-110-Ours | 77.50 | 95.60 |
| WRN-16-8 | 79.57 | 95.73 |
| WRN-16-8-SE | 80.86 | 96.12 |
| WRN-16-8-ours | 80.91 | 96.20 |
| WRN-28-10 | 80.13 | 95.83 |
| WRN-28-10-SAOL | 80.89 | 96.44 |
| WRN-28-10-ABN | 81.88 | 96.22 |
| WRN-28-10-ours | 81.86 | 96.46 |
| ResNext | 81.68 | 96.16 |
| ResNext-ABN | 82.30 | 96.20 |
| ResNext-Ours | 83.02 | 96.43 |
| DenseNet | 77.73 | 95.41 |
| DenseNet-ABN | 78.37 | 95.83 |
| DenseNet-SAOL | 76.84 | 95.31 |
| DenseNet-Ours | 78.41 | 95.51 |
| VGG-16 | 72.18 | 92.64 |
| VGG-16-ours | 74.67 | 94.29 |
| VGG-11 | 68.64 | 92.00 |
| VGG-11-ours | 72.18 | 92.94 |