השוואת שיטות
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| למידה פעילה עם למידה מונחית-עצמית× | למידת מעט דוגמאות× | |
|---|---|---|
| תחום | למידת מכונה | למידת מכונה |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 2020-2022 | 2011–2017 |
| הוגה השיטה≠ | Multiple authors (active learning + SSL integration, 2020s) | Lake, B. M.; Vinyals, O.; Finn, C. et al. |
| סוג≠ | Hybrid learning paradigm | Meta-learning / low-data learning paradigm |
| מקור מכונן≠ | Bengar, J. Z., van de Weijer, J., Fuentes, L. L., & Raducanu, B. (2022). Class-Balanced Active Learning for Image Classification. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 3082–3091. link ↗ | Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29. link ↗ |
| כינויים | AL-SSL, active self-supervised learning, self-supervised active learning, query-based self-supervised learning | FSL, low-shot learning, k-shot learning, meta-learning for few examples |
| קשורות≠ | 6 | 4 |
| תקציר≠ | Active learning combined with self-supervised learning leverages unlabeled data through self-supervised pre-training to build rich representations, then uses an active query strategy to select the most informative examples for human annotation, maximizing model performance under a tight labeling budget. This hybrid approach is especially powerful when labeled data is scarce but large unlabeled pools exist. | Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited. |
| ScholarGateמערך נתונים ↗ |
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