Linganisha mbinu

	Ujifunzaji wa Vikundi wa Sampuli Chache	Kujifunza kwa Kina kidogo kwa Njia ya Nusu-Simamizi
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2019	2018
Mwanzilishi	Dvornik, N., Schmid, C., & Mairal, J.	Ren, M. et al. (ICLR 2018); builds on Finn et al. (MAML, 2017)
Aina	Ensemble of few-shot learners	Meta-learning with unlabeled auxiliary data
Chanzo asilia	Dvornik, N., Schmid, C., & Mairal, J. (2019). Diversity with Cooperation: Ensemble Methods for Few-Shot Classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3716–3725.	Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., & Zemel, R. S. (2018). Meta-learning for semi-supervised few-shot classification. In International Conference on Learning Representations (ICLR 2018).
Majina mbadala	ensemble few-shot classification, multi-model few-shot learning, few-shot ensemble, cooperative few-shot ensemble	SS-FSL, semi-supervised meta-learning, few-shot learning with unlabeled data, low-label few-shot learning
Zinazohusiana	5	4
Muhtasari	Ensemble Few-Shot Learning combines multiple few-shot models — such as prototypical networks or embedding learners — to classify new classes from only one to a handful of labeled examples. By enforcing diversity among base learners and aggregating their predictions, the ensemble consistently outperforms any single few-shot model in accuracy and robustness, especially under severe label scarcity.	Semi-supervised Few-shot Learning (SS-FSL) trains models to classify new classes from only a handful of labeled examples per class, while simultaneously leveraging a pool of unlabeled data to enrich class representations. By combining meta-learning episodes with soft pseudo-label assignment for unlabeled samples, it achieves notably higher accuracy than purely supervised few-shot methods when abundant unlabeled data is available.