مقایسهٔ روش‌ها

	یادگیری نیمه‌نظارت‌شده	یادگیری خودنظارتی
حوزه	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning
سال پیدایش	1970s–2006 (formalized)	2018–2020
پدیدآور	Vapnik, V. N. and others (community of researchers, 1970s–2000s)	LeCun, Y. and community (formalized ~2018–2020)
نوع	Learning paradigm	Representation learning paradigm
منبع بنیادین	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	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/
نام‌های دیگر	SSL, semi-supervised machine learning, transductive learning, label-efficient learning	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
مرتبط	5	3
خلاصه	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.	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.