EVCL: Elastic Variational Continual Learning with Weight Consolidation
<|reference_start|>EVCL: Elastic Variational Continual Learning with Weight Consolidation: Continual learning aims to allow models to learn new tasks without forgetting what has been learned before. This work introduces Elastic Variational Continual Learning with Weight Consolidation (EVCL), a novel hybrid model that integrates the variational posterior approximation mechanism of Variational Continual Learning (VCL) with the regularization-based parameter-protection strategy of Elastic Weight Consolidation (EWC). By combining the strengths of both methods, EVCL effectively mitigates catastrophic forgetting and enables better capture of dependencies between model parameters and task-specific data. Evaluated on five discriminative tasks, EVCL consistently outperforms existing baselines in both domain-incremental and task-incremental learning scenarios for deep discriminative models.<|reference_end|>
@article{batra2024evcl:,
    title={EVCL: Elastic Variational Continual Learning with Weight Consolidation},
    author={Hunar Batra, Ronald Clark},
    journal={arXiv preprint arXiv:2406.15972},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15972},
    primaryClass={cs.LG stat.ML}
}