Pruning on CIFAR-100 (test)
0.669FLOPs-based Accuracy-Retention AUCBest local-axis method
Evaluation Results
| Method | Links | |
|---|---|---|
| Best local-axis methodBackbone=ResNet-182026.05 | 0.669 | |
| Random channel pruningBackbone=ResNet-182026.05 | 0.614 | |
| Best target-axis methodBackbone=ResNet-182026.05 | 0.606 | |
| MagnitudeBackbone=VGG-162026.05 | 0.563 | |
| FPGM / geometric medianBackbone=VGG-162026.05 | 0.561 | |
| Network SlimmingBackbone=ResNet-182026.05 | 0.546 | |
| Network SlimmingBackbone=VGG-162026.05 | 0.525 | |
| Random channel pruningBackbone=VGG-162026.05 | 0.523 | |
| Best local-axis methodBackbone=VGG-162026.05 | 0.483 | |
| TaylorBackbone=VGG-162026.05 | 0.482 | |
| Best target-axis methodBackbone=VGG-162026.05 | 0.448 | |
| Best local-axis methodBackbone=MobileNetV22026.05 | 0.42 | |
| FPGM / geometric medianBackbone=ResNet-182026.05 | 0.396 | |
| MagnitudeBackbone=ResNet-182026.05 | 0.393 | |
| TaylorBackbone=ResNet-182026.05 | 0.349 | |
| Best target-axis methodBackbone=MobileNetV22026.05 | 0.346 | |
| TaylorBackbone=MobileNetV22026.05 | 0.329 | |
| Activation RMSBackbone=MobileNetV22026.05 | 0.32 | |
| FPGM / geometric medianBackbone=MobileNetV22026.05 | 0.315 | |
| MagnitudeBackbone=MobileNetV22026.05 | 0.313 | |
| Random channel pruningBackbone=MobileNetV22026.05 | 0.285 | |
| CHIPBackbone=MobileNetV22026.05 | 0.284 | |
| Network SlimmingBackbone=MobileNetV22026.05 | 0.267 | |
| Activation RMSBackbone=ResNet-182026.05 | 0.243 | |
| Activation RMSBackbone=VGG-162026.05 | 0.14 | |
| CHIPBackbone=ResNet-182026.05 | 0.13 | |
| CHIPBackbone=VGG-162026.05 | 0.108 |