পদ্ধতির তুলনা করুন

	ট্রান্সফার লার্নিং	আধা-তত্ত্বাবধানাধীন শিখন (Semi-supervised Learning)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2010 (formalized); 1990s (early roots)	1970s–2006 (formalized)
প্রবর্তক	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
ধরন	Learning paradigm	Learning paradigm
মৌলিক উৎস	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
অপর নাম	TL, domain adaptation, fine-tuning, pre-trained model adaptation	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
সম্পর্কিত	3	5
সারসংক্ষেপ	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.