手法を比較

	アンサンブル転移学習	半教師あり転移学習
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2010s	2010s
提唱者	Various (consolidated in deep learning era, 2010s)	Pan, S. J. & Yang, Q. (formalized); wider community
種類	Ensemble of pre-trained / fine-tuned models	Hybrid learning paradigm
原典	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.	Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), 43–76.
別名	transfer ensemble, multi-model transfer learning, ensemble of fine-tuned models, ETL	SSTL, semi-supervised domain adaptation, transfer learning with unlabeled data, few-label transfer learning
関連	6	4
概要	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.	Semi-supervised Transfer Learning combines knowledge transferred from a richly labeled source domain with the structure of abundant unlabeled target-domain data, using only a small set of labeled target examples to achieve strong generalization where full annotation is scarce or expensive.