Сравнение на методи

	Самообучаващо се учене	Полу-наблюдавано обучение
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2018–2020	1970s–2006 (formalized)
Създател	LeCun, Y. and community (formalized ~2018–2020)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Тип	Representation learning paradigm	Learning paradigm
Основополагащ източник	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/	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Други названия	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Свързани	3	5
Резюме	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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.