Comparer des méthodes

	Apprentissage par peu d'exemples régularisé	Apprentissage auto-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2016-2020	2018–2020
Auteur d'origine	Multiple (Chen et al., Tian et al., Snell et al., and others)	LeCun, Y. and community (formalized ~2018–2020)
Type	Meta-learning framework with explicit regularization	Representation learning paradigm
Source fondatrice	Chen, W., Liu, Y., Kira, Z., Wang, Y. F., & Huang, J. (2019). A Closer Look at Few-Shot Classification. International Conference on Learning Representations (ICLR).	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	FSL with regularization, regularized meta-learning, few-shot learning with regularization, regularized episodic learning	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Apparentées	5	3
Résumé	Regularized few-shot learning augments standard few-shot learning pipelines with explicit regularization mechanisms — such as weight decay, dropout, data augmentation, label smoothing, or manifold constraints — to reduce overfitting to the tiny support sets that define each episode. This produces more generalizable models when only one to thirty labeled examples per class are available.	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.