השוואת שיטות

	למידת העברה מצרפית	למידת מעט דוגמאות
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2010s	2011–2017
הוגה השיטה	Various (consolidated in deep learning era, 2010s)	Lake, B. M.; Vinyals, O.; Finn, C. et al.
סוג	Ensemble of pre-trained / fine-tuned models	Meta-learning / low-data learning paradigm
מקור מכונן	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.
כינויים	transfer ensemble, multi-model transfer learning, ensemble of fine-tuned models, ETL	FSL, low-shot learning, k-shot learning, meta-learning for few examples
קשורות	6	4
תקציר	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.