Continual Learning on Six Benchmark Datasets Overall
73.3Average AccuracyURM (Upper Bound)
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| URM (Upper Bound)2026.05 | 73.3 | — | — | — | |
| ⋆-CL-CORAL2026.05 | 64.7 | 2.8 | 2.1 | 2.5 | |
| ⋆-CL-VREX2026.05 | 63.4 | 4.3 | 4 | 4 | |
| ⋆-CL-MMD2026.05 | 63.1 | 4.5 | 3.6 | 3.5 | |
| ER-ACE2026.05 | 62.8 | 5.2 | 4.1 | 6.5 | |
| STAR2026.05 | 62.1 | 6.5 | 4.8 | 5.5 | |
| FDR2026.05 | 61.2 | 8.5 | 8.1 | 8.5 | |
| ⋆-CL-ANDMask2026.05 | 60.9 | 8.3 | 6.1 | 8.5 | |
| ⋆-CL-Fishr2026.05 | 60.3 | 9.2 | 7.8 | 10 | |
| EFC2026.05 | 59.2 | 8.3 | 7.7 | 9 | |
| AGEM2026.05 | 58.5 | 7.5 | 6.2 | 8.5 | |
| COPE2026.05 | 56.2 | 11 | 10 | 12 | |
| LODE2026.05 | 54.9 | 12.5 | 12.1 | 11.5 | |
| SARL2026.05 | 54 | 13.7 | 11.8 | 16.5 | |
| SI2026.05 | 52.9 | 14.2 | 14.1 | 13.5 | |
| SNR2026.05 | 52.8 | 13.3 | 13 | 14 | |
| EWC2026.05 | 52 | 10.8 | 9.8 | 10.5 | |
| Finetune2026.05 | 50.4 | 14.3 | 14 | 15 | |
| UPGD2026.05 | 48.2 | 16 | 15.8 | 16.5 |