Model Inversion with Layer-Specific Modeling and Alignment for Data-Free Continual Learning

Continual learning (CL) aims to incrementally train a model to a sequence of tasks while maintaining performance on previously seen ones. Despite mitigating forgetting, data storage and replay are often infeasible due to privacy or security constraints and are impractical for arbitrary pre-trained models. Data-free or examplar-free CL aims to continually update models with new tasks without storing previous data. In addition to regularizing updates, we employ model inversion to synthesize data from the trained model, anchoring learned knowledge through replay without retaining old data. However, model inversion in predictive models faces two key challenges.