Comparar métodos

	Aprendizado com Poucos Exemplos	Aprendizado Autossupervisionado
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2011–2017	2018–2020
Autor original	Lake, B. M.; Vinyals, O.; Finn, C. et al.	LeCun, Y. and community (formalized ~2018–2020)
Tipo	Meta-learning / low-data learning paradigm	Representation learning paradigm
Fonte seminal	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.	LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/
Outros nomes	FSL, low-shot learning, k-shot learning, meta-learning for few examples	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Relacionados	4	3
Resumo	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.	Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.