Exemplar-Free Class-Incremental Learning on ImageNet-R 10-task (test)
27.92Average Accuracy (AA)Inf-SSM
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| Inf-SSMBackbone=Vim-tiny2025.05 | 27.92 | 49.25 | 25.18 | |
| LwF-ABCBackbone=Vim-tiny, Regularization focus=parameter sets (A, B, C)2025.05 | 26.17 | 49.25 | 32.25 | |
| EWCBackbone=Vim-tiny, Regularization focus=parameter sets (A, B, C)2025.05 | 23.14 | 47.38 | 55.45 | |
| MASBackbone=Vim-tiny, Regularization focus=parameter sets (A, B, C)2025.05 | 22.41 | 43.43 | 60.23 | |
| SeqBackbone=Vim-tiny, Regularization focus=parameter sets (A, B, C)2025.05 | 17.58 | 40.07 | 63 | |
| SIBackbone=Vim-tiny, Regularization focus=parameter sets (A, B, C)2025.05 | 17.15 | 39.41 | 64.65 |