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arXiv · 2103.15851

Distilled Replay: Overcoming Forgetting through Synthetic Samples

Andrea Rosasco·Antonio Carta·Andrea Cossu·Vincenzo Lomonaco·Davide Bacciu
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Abstract

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.

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Andrea Rosasco, Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, Davide Bacciu. 2021-03-29. Distilled Replay: Overcoming Forgetting through Synthetic Samples. https://arxiv.org/abs/2103.15851

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