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| 자기 지도 능동 학습× | 자기 지도 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2020–2021 | 2018–2020 |
| 창시자≠ | Bengar et al. and concurrent works (multiple groups) | LeCun, Y. and community (formalized ~2018–2020) |
| 유형≠ | Hybrid active-learning and self-supervised pre-training framework | Representation learning paradigm |
| 원전≠ | 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 ↗ | LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/ link ↗ |
| 별칭 | SSL-AL, self-supervised active learning, semi-supervised active learning with self-supervision, label-efficient self-supervised learning | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning |
| 관련≠ | 5 | 3 |
| 요약≠ | 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. | Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples. |
| ScholarGate데이터셋 ↗ |
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