DSS: A Diverse Sample Selection Method to Preserve Knowledge in Class-Incremental Learning
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 26 references that are in the map. First posted on arXiv 14 Dec 2023.
Builds on
11 of 26 references.
References · 11
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- 2023EmbAu: A Novel Technique to Embed Audio Data Using Shuffled Frog Leaping Algorithmcited by 4
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Citing papers per year: 2023: 1.
1 paper.