So sánh phương pháp

	Học chuyển giao tổ hợp	Học chuyển giao bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2010s	2010s
Người khởi xướng	Various (consolidated in deep learning era, 2010s)	Pan, S. J. & Yang, Q. (formalized); wider community
Loại	Ensemble of pre-trained / fine-tuned models	Hybrid learning paradigm
Công trình gốc	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.
Tên gọi khác	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
Liên quan	6	4
Tóm tắt	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.