Salīdzināt metodes

	Regularizētā pārvietotā mācīšanās	Pārneses apmācība
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2000s–2010s	2010 (formalized); 1990s (early roots)
Autors	Pan, S. J. & Yang, Q. (survey); regularization variants by multiple authors	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Tips	Regularized supervised/semi-supervised learning framework	Learning paradigm
Pirmavots	Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Citi nosaukumi	regularized domain adaptation, transfer learning with regularization, penalized transfer learning, regularized fine-tuning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Saistītās	6	3
Kopsavilkums	Regularized Transfer Learning applies explicit penalty terms to a transfer learning pipeline to control how much a model shifts away from source-domain knowledge when adapting to a new target domain. The regularizer discourages negative transfer — the harmful carry-over of irrelevant source patterns — while preserving beneficial shared representations and preventing overfitting when target-domain labels are scarce.	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.