विधियों की तुलना करें

	ट्रांसफर लर्निंग	अर्ध-पर्यवेक्षित शिक्षण
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2010 (formalized); 1990s (early roots)	1970s–2006 (formalized)
प्रवर्तक	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
प्रकार	Learning paradigm	Learning paradigm
मौलिक स्रोत	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
उपनाम	TL, domain adaptation, fine-tuning, pre-trained model adaptation	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
संबंधित	3	5
सारांश	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.	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.