Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929680364339200 |
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| author | Doucet, Paul Estermann, Benjamin Aczel, Till Wattenhofer, Roger |
| author_facet | Doucet, Paul Estermann, Benjamin Aczel, Till Wattenhofer, Roger |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_03728 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training Doucet, Paul Estermann, Benjamin Aczel, Till Wattenhofer, Roger Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.03728 |