Comparer des méthodes

	Apprentissage en ligne	Apprentissage auto-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1958–2000s	2018–2020
Auteur d'origine	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)	LeCun, Y. and community (formalized ~2018–2020)
Type	Learning paradigm (sequential model update)	Representation learning paradigm
Source fondatrice	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	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/
Alias	incremental learning, sequential learning, streaming learning, online machine learning	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Apparentées	6	3
Résumé	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.	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.