Image Classification on CIFAR-10 (test) (Sparsity)
99.6SparsityContinuous Sparsification
Evaluation Results
| Method | Links | |
|---|---|---|
| Continuous SparsificationBackbone=VGG-16, Accuracy constraint=Within 2% of original dense model2019.12 | 99.6 | |
| Network SlimmingBackbone=VGG-16, Accuracy constraint=Within 2% of original dense model2019.12 | 99 | |
| GMPBackbone=VGG-16, Accuracy constraint=Within 2% of original dense model2019.12 | 98 | |
| Magnitude PruningBackbone=VGG-16, Accuracy constraint=Within 2% of original dense model2019.12 | 97.5 | |
| Continuous SparsificationBackbone=ResNet-20, Accuracy constraint=Within 2% of original dense model2019.12 | 94.4 | |
| GMPBackbone=ResNet-20, Accuracy constraint=Within 2% of original dense model2019.12 | 86 | |
| AMCBackbone=VGG-16, Accuracy constraint=Within 2% of original dense model2019.12 | 86 | |
| Network SlimmingBackbone=ResNet-20, Accuracy constraint=Within 2% of original dense model2019.12 | 85 | |
| Magnitude PruningBackbone=ResNet-20, Accuracy constraint=Within 2% of original dense model2019.12 | 80 | |
| AMCBackbone=ResNet-20, Accuracy constraint=Within 2% of original dense model2019.12 | 50 | |
| Louizos et al.Backbone=VGG-16, Accuracy constraint=Within 2% of original dense model2019.12 | 18.2 | |
| Louizos et al.Backbone=ResNet-20, Accuracy constraint=Within 2% of original dense model2019.12 | 13.6 |