Comparar métodos

	Aprendizado Autossupervisionado	Rede neural siamesa
Área	Aprendizado de máquina	Aprendizado profundo
Família	Machine learning	Machine learning
Ano de origem	2018–2020	1993
Autor original	LeCun, Y. and community (formalized ~2018–2020)	Jane Bromley & Yann LeCun et al.; popularized by Koch et al.
Tipo	Representation learning paradigm	Deep metric-learning architecture
Fonte seminal	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/	Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., & Shah, R. (1993). Signature verification using a 'Siamese' time delay neural network. Advances in Neural Information Processing Systems, 6.
Outros nomes	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning	twin network, Siamese neural network, contrastive metric network, Siyam ağı
Relacionados	3	1
Resumo	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.	A Siamese network is a deep architecture with two (or more) identical, weight-sharing branches that map inputs into an embedding space where similar inputs land close together and dissimilar ones far apart. Introduced by Bromley, LeCun, and colleagues in 1993 for signature verification and revived by Koch et al. (2015) for one-shot image recognition, it learns a similarity metric rather than fixed class labels, making it ideal for verification, matching, and few-shot tasks.