So sánh phương pháp

	Học tăng cường ít mẫu có điều hòa	Học tăng cường tự giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2016-2020	2018–2020
Người khởi xướng	Multiple (Chen et al., Tian et al., Snell et al., and others)	LeCun, Y. and community (formalized ~2018–2020)
Loại	Meta-learning framework with explicit regularization	Representation learning paradigm
Công trình gốc	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/
Tên gọi khác	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
Liên quan	5	3
Tóm tắt	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.