Samples Selection with Group Metric for Experience Replay in Continual Learning

Andrii Krutsylo

PhD student at the Institute of Computer Science of the Polish Academy of Sciences

The study aims to reduce the decline in performance of a model trained incrementally on non-i.i.d. data, using replay-based strategies to retain previous task knowledge. To address limitations in existing variations, which only select samples based on individual properties, a new metric will be introduced to evaluate and choose the optimal replay batch from the memory buffer. This will address the issue of selecting a set of seemingly optimal individual samples, but not the best set as a whole.

Keywords: continual learning, experience replay, data complexity, feature selection, batch attention, anomaly detection