  Modern machine learning systems need to be able to cope with constantly
arriving and changing data. Two main areas of research dealing with such
scenarios are continual learning and data stream mining. Continual learning
focuses on accumulating knowledge and avoiding forgetting, assuming information
once learned should be stored. Data stream mining focuses on adaptation to
concept drift and discarding outdated information, assuming that only the most
recent data is relevant. While these two areas are mainly being developed in
separation, they offer complementary views on the problem of learning from
dynamic data. There is a need for unifying them, by offering architectures
capable of both learning and storing new information, as well as revisiting and
adapting to changes in previously seen concepts. We propose a novel continual
learning approach that can handle both tasks. Our experience replay method is
fueled by a centroid-driven memory storing diverse instances of incrementally
arriving classes. This is enhanced with a reactive subspace buffer that tracks
concept drift occurrences in previously seen classes and adapts clusters
accordingly. The proposed architecture is thus capable of both remembering
valid and forgetting outdated information, offering a holistic framework for
continual learning under concept drift.
