Distilled Replay: Overcoming Forgetting through Synthetic Samples
This addresses memory efficiency in continual learning for AI systems, though it is incremental as it builds on existing replay strategies.
The paper tackled catastrophic forgetting in continual learning by introducing Distilled Replay, a method that uses a small buffer of one pattern per class, achieving competitive performance on four benchmarks.
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.