Fast and Slow Variational Continual Learning

The paper introduces the Continual IVON (CoVON) optimizer, which integrates fast and slow adaptation mechanisms into the Variational Continual Learning (VCL) framework to enhance continual learning in deep networks. By merging past posteriors to create a stable prior for fast-weight updates, CoVON demonstrates superior performance over existing VCL optimizers and traditional weight-regularization methods in domain-incremental learning and fine-tuning of large language models. This advancement is significant for practitioners as it provides a more effective optimization strategy for maintaining model performance during continual learning scenarios.