DSS: A Diverse Sample Selection Method to Preserve Knowledge in Class-Incremental Learning
This addresses the problem of preserving knowledge in incremental learning for AI systems, though it appears incremental as it builds on rehearsal-based techniques.
The paper tackles catastrophic forgetting in class-incremental learning by proposing DSS, a diverse sample selection method that outperforms state-of-the-art techniques in both disjoint and fuzzy task boundary scenarios.
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.