השוואת שיטות
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| למידה פעילה עם למידה מונחית-עצמית× | למידה פעילה× | |
|---|---|---|
| תחום | למידת מכונה | למידת מכונה |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 2020-2022 | 2009 |
| הוגה השיטה≠ | Multiple authors (active learning + SSL integration, 2020s) | Burr Settles |
| סוג≠ | Hybrid learning paradigm | Interactive supervised learning framework |
| 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 ↗ | Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648. link ↗ |
| כינויים | AL-SSL, active self-supervised learning, self-supervised active learning, query-based self-supervised learning | Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme |
| קשורות≠ | 6 | 2 |
| 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. | Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires. |
| ScholarGateמערך נתונים ↗ |
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