Top-1 Accuracy on ImageNet-1K (val)
64.69Top-1 AccuracyTDDS
Evaluation Results
| Method | Links | |
|---|---|---|
| TDDSPruning Rate (p)=70%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 64.69 | |
| ForgettingPruning Rate (p)=70%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 64.29 | |
| RandomPruning Rate (p)=70%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 64.19 | |
| ModeratePruning Rate (p)=70%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 64.04 | |
| DAPIPC=50, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 62.7 | |
| TDDSPruning Rate (p)=80%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 62.56 | |
| EntropyPruning Rate (p)=70%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 62.34 | |
| ForgettingPruning Rate (p)=80%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 62.01 | |
| ModeratePruning Rate (p)=80%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 61.35 | |
| RandomPruning Rate (p)=80%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 60.76 | |
| VLCPIPC=50, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 60.5 | |
| MGD3IPC=50, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 60.2 | |
| IGDIPC=50, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 59.8 | |
| D3HRIPC=50, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 59.4 | |
| MinimaxIPC=50, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 58.6 | |
| EntropyPruning Rate (p)=80%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 56.8 | |
| RDEDIPC=50, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 56.5 | |
| TDDSPruning Rate (p)=90%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 53.91 | |
| DiTIPC=50, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 52.9 | |
| RandomPruning Rate (p)=90%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 52.63 | |
| ModeratePruning Rate (p)=90%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 52.45 | |
| ForgettingPruning Rate (p)=90%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 52.14 | |
| G-VBSMIPC=50, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 51.8 | |
| DAPIPC=10, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 49.1 | |
| EL2NPruning Rate (p)=70%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 46.92 | |
| SRe2LIPC=50, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 46.8 | |
| VLCPIPC=10, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 46.7 | |
| MGD3IPC=10, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 45.6 | |
| IGDIPC=10, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 45.5 | |
| MinimaxIPC=10, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 44.3 | |
| D3HRIPC=10, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 44.3 | |
| EntropyPruning Rate (p)=90%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 43.39 | |
| RDEDIPC=10, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 42 | |
| DiTIPC=10, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 39.6 | |
| AUMPruning Rate (p)=70%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 39.34 | |
| EL2NPruning Rate (p)=80%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 32.68 | |
| G-VBSMIPC=10, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 31.4 | |
| AUMPruning Rate (p)=80%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 23.64 | |
| SRe2LIPC=10, Evaluation Protocol=soft-label, Model Architecture=ResNet-182025.10 | 21.3 | |
| EL2NPruning Rate (p)=90%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 15.9 | |
| AUMPruning Rate (p)=90%, Backbone=ResNet-34, Training Strategy=Strategy-E2023.11 | 11.7 |