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SUPClust: Active Learning at the Boundaries
Active learning is a machine learning paradigm designed to optimize model performance in a setting where labeled data is expensive to acquire.
Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training
This study addresses the integration of diversity-based and uncertainty-based sampling strategies in active learning, particularly within the context of self-supervised pre-trained models.
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