قارن الطرق

	تعلم النقل الجماعي	التعلم النقلي شبه المُشرف عليه
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2010s	2010s
صاحب الطريقة	Various (consolidated in deep learning era, 2010s)	Pan, S. J. & Yang, Q. (formalized); wider community
النوع	Ensemble of pre-trained / fine-tuned models	Hybrid learning paradigm
المصدر التأسيسي	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.
الأسماء البديلة	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
ذات صلة	6	4
الملخص	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.