Evaluating the Impact of Task Granularity on Catastrophic Forgetting in Continual Learning
This study investigates the impact of task granularity ordering on catastrophic forgetting in continual learning, presenting the first systematic evaluation of three learning strategies—coarse-to-fine, fine-to-coarse, and flat learning—on CIFAR-100. Leveraging Elastic Weight Consolidation (EWC), the authors assess model performance using accuracy, F1 score, and continual learning–specific metrics to quantify the retention of previously acquired knowledge. The findings demonstrate that initializing learning with coarse-grained categories before introducing fine-grained tasks significantly mitigates catastrophic forgetting and enhances backward transfer. This suggests that incorporating hierarchical priors to construct stable representations offers an effective principle for designing task sequences in incremental learning scenarios, thereby providing a novel strategy for optimizing continual learning systems.