So sánh phương pháp

	Học đo lường tự giám sát	Học tăng cường tự giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2020 (modern contrastive formulation); foundations 1990s–2000s	2018–2020
Người khởi xướng	Chen, T. et al. (SimCLR); earlier metric learning foundations by Bromley, LeCun (1994)	LeCun, Y. and community (formalized ~2018–2020)
Loại	Self-supervised representation learning with metric objective	Representation learning paradigm
Công trình gốc	Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML 2020), PMLR 119, 1597–1607.	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	self-supervised representation learning with metric loss, contrastive self-supervised learning, unsupervised metric learning, SSML	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Liên quan	3	3
Tóm tắt	Self-supervised metric learning trains a neural encoder to embed inputs so that semantically similar items lie close together in vector space, using automatically generated pseudo-labels instead of human annotations. By combining self-supervised pretext tasks with contrastive or triplet-based metric objectives, it produces transferable, label-efficient representations applicable to retrieval, clustering, and few-shot classification.	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.