EVCL: Elastic Variational Continual Learning with Weight Consolidation

• URL: http://arxiv.org/abs/2406.15972v1
• Date: Sun, 23 Jun 2024 00:32:06 GMT
• Title: EVCL: Elastic Variational Continual Learning with Weight Consolidation
• Authors: Hunar Batra, Ronald Clark,
• Abstract summary: 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 (E), a novel hybrid model that integrates the variational posterior approximation mechanism of Variational Continual Learning (EWC) with the regularization-based parameter-protection strategy of Elastic Weight Consolidation (EWC) E effectively mitigates catastrophic forgetting and enables better capture of dependencies between model parameters and task-specific data.
• Score: 14.485182089870928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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