  Many real-world applications require machine-learning models to be able to
deal with non-stationary data distributions and thus learn autonomously over an
extended period of time, often in an online setting. One of the main challenges
in this scenario is the so-called catastrophic forgetting (CF) for which the
learning model tends to focus on the most recent tasks while experiencing
predictive degradation on older ones. In the online setting, the most effective
solutions employ a fixed-size memory buffer to store old samples used for
replay when training on new tasks. Many approaches have been presented to
tackle this problem. However, it is not clear how predictive uncertainty
information for memory management can be leveraged in the most effective manner
and conflicting strategies are proposed to populate the memory. Are the
easiest-to-forget or the easiest-to-remember samples more effective in
combating CF? Starting from the intuition that predictive uncertainty provides
an idea of the samples' location in the decision space, this work presents an
in-depth analysis of different uncertainty estimates and strategies for
populating the memory. The investigation provides a better understanding of the
characteristics data points should have for alleviating CF. Then, we propose an
alternative method for estimating predictive uncertainty via the generalised
variance induced by the negative log-likelihood. Finally, we demonstrate that
the use of predictive uncertainty measures helps in reducing CF in different
settings.
