Comparar métodos

	Aprendizaje Semi-supervisado de Pocas Muestras	Aprendizaje con Pocos Ejemplos
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	2018	2011–2017
Autor original	Ren, M. et al. (ICLR 2018); builds on Finn et al. (MAML, 2017)	Lake, B. M.; Vinyals, O.; Finn, C. et al.
Tipo	Meta-learning with unlabeled auxiliary data	Meta-learning / low-data learning paradigm
Fuente seminal	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).	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.
Alias	SS-FSL, semi-supervised meta-learning, few-shot learning with unlabeled data, low-label few-shot learning	FSL, low-shot learning, k-shot learning, meta-learning for few examples
Relacionados	4	4
Resumen	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.	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.