Exemplar-Free Class-Incremental Learning on Caltech-256 5-Tasks
50.75Average Accuracy (AA)Inf-SSM
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| Inf-SSMBackbone=Vim-Small, Regularization focus=geometry-aware2025.05 | 50.75 | 67.04 | 49.93 | |
| Inf-SSMBackbone=Vim-small2025.05 | 50.75 | 67.04 | 49.93 | |
| SIBackbone=Vim-Small, Regularization focus=(A, B, C)2025.05 | 47.57 | 65.29 | 57.88 | |
| SIBackbone=Vim-small2025.05 | 47.57 | 65.29 | 57.88 | |
| LwF-ABCBackbone=Vim-Small, Regularization focus=(A, B, C)2025.05 | 46.52 | 66.58 | 59.03 | |
| LwF-ABCBackbone=Vim-small2025.05 | 46.52 | 66.58 | 59.03 | |
| MASBackbone=Vim-Small, Regularization focus=(A, B, C)2025.05 | 44.87 | 66.44 | 61 | |
| MASBackbone=Vim-small2025.05 | 44.87 | 66.44 | 61 | |
| UCLBackbone=Vim-small2025.05 | 44.74 | 65.17 | 62.48 | |
| EWCBackbone=Vim-Small, Regularization focus=(A, B, C)2025.05 | 42.93 | 64.27 | 64.3 | |
| EWCBackbone=Vim-small2025.05 | 42.93 | 64.27 | 64.3 | |
| SeqBackbone=Vim-Small, Regularization focus=(A, B, C)2025.05 | 37.58 | 60.17 | 71.48 | |
| SeqBackbone=Vim-small2025.05 | 37.58 | 60.17 | 71.48 |