Replay strategies are Continual Learning techniques which mitigate catastrophic URLetting 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 URLetting 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.
Replay strategies are Continual Learning techniques which mitigate catastrophic URLetting 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 URLetting 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.
R
experience
experiences
D
up to
D
naive replay, which randomly samples patterns from the dataset, on four
A
replay-based strategies on four