  Existing approaches on continual learning 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 paper
proposes a few-shot continual learning 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 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
\url{https://github.com/anwarmaxsum/FLOWER}.
