Relative Parameter Importance in Task-Agnostic Replay-Free Continual Learning

Abstract:Achieving continual learning (CL) with deep neural networks requires balancing stability and plasticity while enabling knowledge transfer. In this work, we focus on offline learning algorithms under the constraints: (I) no access to training data from prior tasks (II) no access to task-id at inference time. We introduce a novel measure, the relative parameter-importance, which measures the relative importance of each parameter with respect to both the current and past tasks. Parameters with high relative importance are interpreted as more important for maintaining past-task stability and thus heavily regularised, whereas parameters with low relative-importance are allowed to be more freely updated. Unlike existing methods, our approach allows the update of parameters with high past-task importance when they have low relative-importance, thus enabling backward knowledge transfer in addition to tackling the stability-plasticity trade-off. We demonstrate improvements against state-of-the-art CL methods on both class-incremental and domain-incremental learning text classification problems and provide insights for extending our method to text generation problems. Code available at: this https URL

Machine Learning (cs.LG)

arXiv:2608.00630 [cs.LG]

(or arXiv:2608.00630v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.00630 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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