Image Classification on Tiny ImageNet 64x64 (test)
29.6AccuracyNCFM
Evaluation Results
| Method | Links | |
|---|---|---|
| NCFMIPC=50, Ratio (%)=10, Backbone=4-layer ConvNet2025.02 | 29.6 | |
| MTTIPC=50, Ratio (%)=10, Backbone=4-layer ConvNet2025.02 | 28 | |
| IDMIPC=50, Ratio (%)=10, Backbone=4-layer ConvNet2025.02 | 27.7 | |
| NCFMIPC=10, Ratio (%)=2, Backbone=4-layer ConvNet2025.02 | 26.8 | |
| ATTIPC=10, Ratio (%)=2, Backbone=4-layer ConvNet2025.02 | 25.8 | |
| FrePoIPC=10, Ratio (%)=2, Backbone=4-layer ConvNet2025.02 | 25.4 | |
| FTDIPC=10, Ratio (%)=2, Backbone=4-layer ConvNet2025.02 | 24.5 | |
| DMIPC=50, Ratio (%)=10, Backbone=4-layer ConvNet2025.02 | 24.1 | |
| MTTIPC=10, Ratio (%)=2, Backbone=4-layer ConvNet2025.02 | 23.2 | |
| IDMIPC=10, Ratio (%)=2, Backbone=4-layer ConvNet2025.02 | 21.9 | |
| NCFMIPC=1, Ratio (%)=0.2, Backbone=4-layer ConvNet2025.02 | 18.2 | |
| HerdingIPC=50, Ratio (%)=10, Backbone=4-layer ConvNet2025.02 | 16.7 | |
| FrePoIPC=1, Ratio (%)=0.2, Backbone=4-layer ConvNet2025.02 | 15.4 | |
| RandomIPC=50, Ratio (%)=10, Backbone=4-layer ConvNet2025.02 | 15 | |
| ForgettingIPC=50, Ratio (%)=10, Backbone=4-layer ConvNet2025.02 | 15 | |
| DMIPC=10, Ratio (%)=2, Backbone=4-layer ConvNet2025.02 | 12.9 | |
| ATTIPC=1, Ratio (%)=0.2, Backbone=4-layer ConvNet2025.02 | 11 | |
| FTDIPC=1, Ratio (%)=0.2, Backbone=4-layer ConvNet2025.02 | 10.4 | |
| IDMIPC=1, Ratio (%)=0.2, Backbone=4-layer ConvNet2025.02 | 10.1 | |
| MTTIPC=1, Ratio (%)=0.2, Backbone=4-layer ConvNet2025.02 | 8.8 | |
| HerdingIPC=10, Ratio (%)=2, Backbone=4-layer ConvNet2025.02 | 6.3 | |
| ForgettingIPC=10, Ratio (%)=2, Backbone=4-layer ConvNet2025.02 | 5.1 | |
| RandomIPC=10, Ratio (%)=2, Backbone=4-layer ConvNet2025.02 | 5 | |
| DMIPC=1, Ratio (%)=0.2, Backbone=4-layer ConvNet2025.02 | 3.9 | |
| HerdingIPC=1, Ratio (%)=0.2, Backbone=4-layer ConvNet2025.02 | 2.8 | |
| ForgettingIPC=1, Ratio (%)=0.2, Backbone=4-layer ConvNet2025.02 | 1.6 | |
| RandomIPC=1, Ratio (%)=0.2, Backbone=4-layer ConvNet2025.02 | 1.4 |