Exemplar-Free Class-Incremental Learning on Caltech-256 10-Tasks
39.88Average Accuracy (AA)Inf-SSM
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| Inf-SSMBackbone=Vim-Small, Regularization focus=geometry-aware2025.05 | 39.88 | 62.28 | 55.85 | |
| Inf-SSMBackbone=Vim-small2025.05 | 39.88 | 62.28 | 55.85 | |
| LwF-ABCBackbone=Vim-Small, Regularization focus=(A, B, C)2025.05 | 35.45 | 59.63 | 64.32 | |
| LwF-ABCBackbone=Vim-small2025.05 | 35.45 | 59.63 | 64.32 | |
| UCLBackbone=Vim-small2025.05 | 33.16 | 57.95 | 69.07 | |
| EWCBackbone=Vim-Small, Regularization focus=(A, B, C)2025.05 | 28.35 | 56.64 | 72.73 | |
| EwCBackbone=Vim-small2025.05 | 28.35 | 56.64 | 72.73 | |
| MASBackbone=Vim-Small, Regularization focus=(A, B, C)2025.05 | 28.15 | 55.25 | 73.5 | |
| MASBackbone=Vim-small2025.05 | 28.15 | 55.25 | 73.5 | |
| SIBackbone=Vim-Small, Regularization focus=(A, B, C)2025.05 | 27.66 | 54.34 | 74.69 | |
| SIBackbone=Vim-small2025.05 | 27.66 | 54.34 | 74.69 | |
| SeqBackbone=Vim-Small, Regularization focus=(A, B, C)2025.05 | 24.27 | 51.06 | 79.01 | |
| SeqBackbone=Vim-small2025.05 | 24.27 | 51.06 | 79.01 |