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Continual Learning on VTAB-2

Continual Learning on VTAB-2

86.76Avg Acc (A-bar)EASE

Updated 8mo ago

Evaluation results

Method	Links
EASE2024.03		86.76	78.86
SEMA2024.03		85.99	80.06

State-of-the-art paper

EASE

Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning

Dataset

VTAB

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Related benchmarks

Continual Learning on CIFAR-100 (Training Time)Continual Learning on ImageNet A Training Time on VTAB (Continual Learning)Class-Incremental Learning on ImageNet-R 50 tasks (test)Class-Incremental Learning on ImageNet-A 50 tasks (test)

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