Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training

• URL: http://arxiv.org/abs/2403.03728v1
• Date: Wed, 6 Mar 2024 14:18:24 GMT
• Title: Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training
• Authors: Paul Doucet, Benjamin Estermann, Till Aczel, Roger Wattenhofer
• Abstract summary: This study addresses the integration of diversity-based and uncertainty-based sampling strategies in active learning. We introduce a straightforward called TCM that mitigates the cold start problem while maintaining strong performance across various data levels.
• Score: 23.573986817769025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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