  Replay strategies are Continual Learning techniques which mitigate
catastrophic forgetting by keeping a buffer of patterns from previous
experience, which are interleaved with new data during training. The amount of
patterns stored in the buffer is a critical parameter which largely influences
the final performance and the memory footprint of the approach. This work
introduces Distilled Replay, a novel replay strategy for Continual Learning
which is able to mitigate forgetting by keeping a very small buffer (up to $1$
pattern per class) of highly informative samples. Distilled Replay builds the
buffer through a distillation process which compresses a large dataset into a
tiny set of informative examples. We show the effectiveness of our Distilled
Replay against naive replay, which randomly samples patterns from the dataset,
on four popular Continual Learning benchmarks.
