Sammenlign metoder

	Ensemble Transfer Learning	Overførselslæring
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2010s	2010 (formalized); 1990s (early roots)
Ophavsperson	Various (consolidated in deep learning era, 2010s)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Type	Ensemble of pre-trained / fine-tuned models	Learning paradigm
Oprindelig kilde	Ganaie, M. A., Hu, M., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Aliasser	transfer ensemble, multi-model transfer learning, ensemble of fine-tuned models, ETL	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Relaterede	6	3
Resumé	Ensemble Transfer Learning combines multiple models that were each pre-trained on a large source domain and then fine-tuned on a target task. By aggregating the predictions of several independently fine-tuned models, it achieves higher accuracy and robustness than any single transferred model alone, especially when the target dataset is small.	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.