QMNIST

New research suggests fine-tuning regimes significantly impact continual learning evaluations

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…