قارن الطرق

	تعلم النقل الجماعي	تعلم العينات القليلة
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2010s	2011–2017
صاحب الطريقة	Various (consolidated in deep learning era, 2010s)	Lake, B. M.; Vinyals, O.; Finn, C. et al.
النوع	Ensemble of pre-trained / fine-tuned models	Meta-learning / low-data 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.	Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29.
الأسماء البديلة	transfer ensemble, multi-model transfer learning, ensemble of fine-tuned models, ETL	FSL, low-shot learning, k-shot learning, meta-learning for few examples
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.	Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited.