SUPClust: Active Learning at the Boundaries
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Date
2024-05-11
Publication Type
Conference Paper
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yes
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Abstract
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.
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published
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5th Workshop on practical ML for limited/low resource settings
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Publisher
OpenReview
Event
5th Workshop on Practical ML for Limited/Low Resource Settings (PML4LRS@ICLR 2024)
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Subject
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); FOS: Computer and information sciences
Organisational unit
03604 - Wattenhofer, Roger / Wattenhofer, Roger
Notes
Poster presentation on May 11, 2024
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Related publications and datasets
Is new version of: 10.48550/ARXIV.2403.03741