SUPClust: Active Learning at the Boundaries
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Abstract (arXiv)
Active learning is a machine learning paradigm designed to optimize model performance in a setting where labeled data is expensive to acquire. In this work, we propose a novel active learning method called SUPClust that seeks to identify points at the decision boundary between classes. By targeting these points, SUPClust aims to gather information that is most informative for refining the model's prediction of complex decision regions. We demonstrate experimentally that labeling these points leads to strong model performance. This improvement is observed even in scenarios characterized by strong class imbalance.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 17 references that are in the map. First posted on arXiv 6 Mar 2024.
Builds on
11 of 17 references.
References · 11
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
- 2019Learning Imbalanced Datasets with Label-Distribution-Aware Margin Losscited by 989
- 2017Deep Bayesian Active Learning with Image Datacited by 819
- 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 610
- 2022Active Learning on a Budget: Opposite Strategies Suit High and Low Budgetscited by 133
- 2022Active Learning Through a Covering Lenscited by 66
- 2023OmniVec: Learning robust representations with cross modal sharingcited by 44
- 2019Parting with Illusions about Deep Active Learningcited by 38
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Citing papers per year: 2025: 2.
2 papers.