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Rehearsal-based techniques are commonly used to mitigate catastrophicforgetting (CF) in Incremental learning (IL). The quality of the exemplarsselected is important for this purpose and most methods do not ensure theappropriate diversity of the selected exemplars. We propose a new technique"DSS" -- Diverse Selection of Samples from the input data stream in theClass-incremental learning (CIL) setup under both disjoint and fuzzy taskboundary scenarios. Our method outperforms state-of-the-art methods and is muchsimpler to understand and implement.

DSS: A Diverse Sample Selection Method to Preserve Knowledge in Class-Incremental Learning