เปรียบเทียบวิธี

	การเรียนรู้แบบกำกับดูแลตนเอง	การเรียนรู้แบบน้อยตัวอย่าง	การเรียนรู้แบบกึ่งกำกับดูแล	การเรียนรู้แบบถ่ายโอน
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning	Machine learning	Machine learning
ปีกำเนิด	2018–2020	2011–2017	1970s–2006 (formalized)	2010 (formalized); 1990s (early roots)
ผู้ริเริ่ม	LeCun, Y. and community (formalized ~2018–2020)	Lake, B. M.; Vinyals, O.; Finn, C. et al.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
ประเภท	Representation learning paradigm	Meta-learning / low-data learning paradigm	Learning paradigm	Learning paradigm
แหล่งต้นตำรับ	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/	Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
ชื่อเรียกอื่น	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning	FSL, low-shot learning, k-shot learning, meta-learning for few examples	SSL, semi-supervised machine learning, transductive learning, label-efficient learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
ที่เกี่ยวข้อง	3	4	5	3
สรุป	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.	Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited.	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.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.