  Continual learning, the setting where a learning agent is faced with a never
ending stream of data, continues to be a great challenge for modern machine
learning systems. In particular the online or "single-pass through the data"
setting has gained attention recently as a natural setting that is difficult to
tackle. Methods based on replay, either generative or from a stored memory,
have been shown to be effective approaches for continual learning, matching or
exceeding the state of the art in a number of standard benchmarks. These
approaches typically rely on randomly selecting samples from the replay memory
or from a generative model, which is suboptimal. In this work, we consider a
controlled sampling of memories for replay. We retrieve the samples which are
most interfered, i.e. whose prediction will be most negatively impacted by the
foreseen parameters update. We show a formulation for this sampling criterion
in both the generative replay and the experience replay setting, producing
consistent gains in performance and greatly reduced forgetting. We release an
implementation of our method at
https://github.com/optimass/Maximally_Interfered_Retrieval.
