Shapley Neuron Values for Continual Learning: Which Neurons Matter Most?
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Computer Science > Machine Learning
Title:Shapley Neuron Values for Continual Learning: Which Neurons Matter Most?
Abstract:Continual learning enables neural networks to learn tasks sequentially without forgetting previously acquired knowledge. However, neural networks suffer from catastrophic forgetting, where learning new tasks degrades performance on earlier ones. We address this problem with Shapley Neuron Valuation (SNV), a principled framework that quantifies Neuron importance in continual learning, grounded in cooperative game theory. SNV selectively freezes important Neurons while keeping others plastic, enabling buffer-free continual learning without expanding architecture. Experiments on ImageNet-1k show that SNV consistently outperforms existing buffer-free methods. In particular, SNV improves accuracy by +2.88% in the class incremental learning and +6.46% in the task incremental learning scenarios compared to the second baseline.
| Comments: | This paper has been accepted to ICML 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.15877 [cs.LG] |
| (or arXiv:2605.15877v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15877
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Mohammad Ali Vahedifar [view email][v1] Fri, 15 May 2026 11:54:45 UTC (698 KB)
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