Comparer des méthodes

	K-means auto-supervisé	Apprentissage auto-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2018	2018–2020
Auteur d'origine	Caron, M. et al. (DeepCluster framework)	LeCun, Y. and community (formalized ~2018–2020)
Type	Self-supervised clustering	Representation learning paradigm
Source fondatrice	Caron, M., Bojanowski, P., Joulin, A., & Douze, M. (2018). Deep Clustering for Unsupervised Learning of Visual Features. In Proceedings of the European Conference on Computer Vision (ECCV), 132–149.	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	self-supervised clustering with K-means, deep clustering with K-means, unsupervised K-means with pseudo-labels, SSL K-means	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Apparentées	5	3
Résumé	Self-supervised K-means is a clustering technique that combines K-means assignment with self-supervised representation learning. The model alternates between clustering unlabeled data points into K groups and using those cluster assignments as pseudo-labels to refine an underlying feature representation, yielding increasingly coherent clusters without any human-annotated ground truth.	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.