  Continual Learning requires the model to learn from a stream of dynamic,
non-stationary data without forgetting previous knowledge. Several approaches
have been developed in the literature to tackle the Continual Learning
challenge. Among them, Replay approaches have empirically proved to be the most
effective ones. Replay operates by saving some samples in memory which are then
used to rehearse knowledge during training in subsequent tasks. However, an
extensive comparison and deeper understanding of different replay
implementation subtleties is still missing in the literature. The aim of this
work is to compare and analyze existing replay-based strategies and provide
practical recommendations on developing efficient, effective and generally
applicable replay-based strategies. In particular, we investigate the role of
the memory size value, different weighting policies and discuss about the
impact of data augmentation, which allows reaching better performance with
lower memory sizes.
