A new paper argues that the fine-tuning regime, specifically the trainable parameter subspace, is a critical variable in evaluating continual learning methods. Researchers found that the relative performance rankings of standard continual learning methods like EWC, LwF, SI, and GEM can change significantly depending on the chosen fine-tuning depth. Deeper adaptation regimes were associated with increased forgetting, suggesting that current evaluation protocols may not be robust across different fine-tuning setups. AI
影响 Highlights the need for regime-aware evaluation protocols in continual learning research, potentially impacting how future methods are benchmarked.
排序理由 Academic paper published on arXiv discussing a novel evaluation methodology for continual learning.
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