विधियों की तुलना करें

	ट्रांसफर लर्निंग	स्व-पर्यवेक्षित शिक्षण
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2010 (formalized); 1990s (early roots)	2018–2020
प्रवर्तक	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)	LeCun, Y. and community (formalized ~2018–2020)
प्रकार	Learning paradigm	Representation 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.	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/
उपनाम	TL, domain adaptation, fine-tuning, pre-trained model adaptation	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
संबंधित	3	3
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.	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.