PBES: PCA Based Exemplar Sampling Algorithm for Continual Learning
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Abstract (arXiv)
We propose a novel exemplar selection approach based on Principal Component Analysis (PCA) and median sampling, and a neural network training regime in the setting of class-incremental learning. This approach avoids the pitfalls due to outliers in the data and is both simple to implement and use across various incremental machine learning models. It also has independent usage as a sampling algorithm. We achieve better performance compared to state-of-the-art methods.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 22 references that are in the map. First posted on arXiv 14 Dec 2023.
Builds on
12 of 22 references.
References · 12
- 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,333
- 2018Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigencecited by 742
- 2021Rainbow Memory: Continual Learning with a Memory of Diverse Samplescited by 273
- 2020Mnemonics Training: Multi-Class Incremental Learning without Forgettingcited by 221
- 2020Coresets via Bilevel Optimization for Continual Learning and Streamingcited by 192
- 2021Online Coreset Selection for Rehearsal-based Continual Learningcited by 117
- 2020GAN Memory with No Forgettingcited by 84
- 2020Incremental Learning In Online Scenariocited by 71
- 2020KeepAugment: A Simple Information-Preserving Data Augmentation Approachcited by 52
- 2018Revisiting Distillation and Incremental Classifier Learningcited by 19
- 2023EmbAu: A Novel Technique to Embed Audio Data Using Shuffled Frog Leaping Algorithmcited by 4
- 2021Principal Gradient Direction and Confidence Reservoir Sampling for Continual Learningcited by 2
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Citing papers per year: 2023: 1.
1 paper.