  Replay strategies are Continual Learning techniques which mitigate
catastrophic forgetting by keeping a buffer of patterns from previous
experiences, 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 (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 popular replay-based strategies on four Continual Learning benchmarks.
