Improving Replay Sample Selection and Storage for Less Forgetting in
  Continual Learning
Daniel Brignac Niels Lobo Abhijit Mahalanobis
Continual learning seeks to enable deep learners to train on a series of
tasks of unknown length without suffering from the catastrophic forgetting of
previous tasks. One effective solution is replay, which involves storing few
previous experiences in memory and replaying them when learning the current
task. However, there is still room for improvement when it comes to selecting
the most informative samples for storage and determining the optimal number of
samples to be stored. This study aims to address these issues with a novel
comparison of the commonly used reservoir sampling to various alternative
population strategies and providing a novel detailed analysis of how to find
the optimal number of stored samples.