AI RESEARCH
Elastic Weight Consolidation Done Right for Continual Learning
arXiv CS.AI
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ArXi:2603.18596v1 Announce Type: cross Weight regularization methods in continual learning (CL) alleviate catastrophic forgetting by assessing and penalizing changes to important model weights. Elastic Weight Consolidation (EWC) is a foundational and widely used approach within this framework that estimates weight importance based on gradients. However, it has consistently shown suboptimal performance. In this paper, we conduct a systematic analysis of importance estimation in EWC from a gradient-based perspective.