Linganisha mbinu

	Kujifunza kwa Vitendo kwa Kutumia Kujifunza Kujisimamia Kwenyewe	Kujifunza kwa Kiasi Kidogo cha Mifano
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2020-2022	2011–2017
Mwanzilishi	Multiple authors (active learning + SSL integration, 2020s)	Lake, B. M.; Vinyals, O.; Finn, C. et al.
Aina	Hybrid learning paradigm	Meta-learning / low-data learning paradigm
Chanzo asilia	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.	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.
Majina mbadala	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
Zinazohusiana	6	4
Muhtasari	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.