Few-Shot Continual Learning via Flat-to-Wide Approaches

The existing approaches on continual learning (CL) call for a lot of samples in their training processes. Such approaches are impractical for many real-world problems having limited samples because of the overfitting problem. This article proposes a few-shot CL approach, termed flat-to-wide approach (FLOWER), where a flat-to-wide learning process finding the flat–wide minima is proposed to address the catastrophic forgetting (CF) problem. The issue of data scarcity is overcome with a data augmentation approach making use of a ball-generator concept to restrict the sampling space into the smallest enclosing ball. Our numerical studies demonstrate the advantage of FLOWER achieving significantly improved performances over prior arts notably in the small base tasks. For further study, source codes of FLOWER, competitor algorithms, and experimental logs are shared publicly in https://github.com/anwarmaxsum/FLOWER.