Shapley Neuron Values for Continual Learning: Which Neurons Matter Most?

摘要

Continual learning enables neural networks to learn tasks sequentially without forgetting previously acquired knowledge. However, catastrophic forgetting, where performance on earlier tasks degrades sharply when learning new ones, remains a fundamental challenge. We address this problem with Shapley Neuron Valuation (SNV), a principled framework grounded in cooperative game theory that quantifies Neuron importance in continual learning. By selectively freezing important Neurons while keeping others plastic, SNV enables memory-free continual learning without architectural expansion. Extensive experiments show that SNV delivers substantial gains over memory-free baselines, achieving +19.50% accuracy on CIFAR-100 and +17.20% on TinyImageNet in the Class-IL setting. In Task-IL scenarios, SNV consistently surpasses existing memory-free approaches by large margins, reaching up to 9.08% higher accuracy on CIFAR-100 compared to the second-best memory-free method, while remaining competitive in comparison with memory-based methods.