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Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training
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Date
2024-05-11Type
- Other Conference Item
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Abstract
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. We introduce a straightforward heuristic called TCM that mitigates the cold start problem while maintaining strong performance across various data levels. By initially applying TypiClust for diversity sampling and subsequently transitioning to uncertainty sampling with Margin, our approach effectively combines the strengths of both strategies. Our experiments demonstrate that TCM consistently outperforms existing methods across various datasets in both low and high data regimes. Show more
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Subject
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); FOS: Computer and information sciencesOrganisational unit
03604 - Wattenhofer, Roger / Wattenhofer, Roger
Related publications and datasets
Is new version of: https://doi.org/10.48550/ARXIV.2403.03728
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ETH Bibliography
yes
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