方法对比
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| 自监督主动学习× | 标签传播× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2020–2021 | 2002 |
| 提出者≠ | Bengar et al. and concurrent works (multiple groups) | Zhu, X. & Ghahramani, Z. |
| 类型≠ | Hybrid active-learning and self-supervised pre-training framework | Graph-based semi-supervised classification |
| 开创性文献≠ | Bengar, J. Z., van de Weijer, J., Twardowski, B., & Raducanu, B. (2021). Reducing Label Effort: Self-Supervised Meets Active Learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 1631–1639. link ↗ | Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ |
| 别名 | SSL-AL, self-supervised active learning, semi-supervised active learning with self-supervision, label-efficient self-supervised learning | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation |
| 相关≠ | 5 | 3 |
| 摘要≠ | Self-supervised Active Learning (SSL-AL) is a label-efficient machine-learning paradigm that pre-trains a model on unlabeled data using self-supervised objectives, then strategically queries a human oracle for the most informative labels using an active-learning acquisition function. The result is strong predictive performance with a fraction of the annotation cost required by fully supervised approaches. | Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data. |
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