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

Rehearsal-based techniques are commonly used to mitigate catastrophic forgetting (CF) in Incremental learning (IL). The quality of the exemplars selected is important for this purpose and most methods do not ensure the appropriate diversity of the selected exemplars. We propose a new technique "DSS" - Diverse Selection of Samples from the input data stream in the Class-incremental learning (CIL) setup under both disjoint and fuzzy task boundary scenarios. Our method outperforms state-of-the-art methods and is much simpler to understand and implement. In an incremental learning (IL) scenario, learning is done on a continuous stream of data, rather than on a batch of data.