विधियों की तुलना करें

	एन्सेम्बल ट्रांसफर लर्निंग	फ्यू-शॉट लर्निंग
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2010s	2011–2017
प्रवर्तक	Various (consolidated in deep learning era, 2010s)	Lake, B. M.; Vinyals, O.; Finn, C. et al.
प्रकार	Ensemble of pre-trained / fine-tuned models	Meta-learning / low-data learning paradigm
मौलिक स्रोत	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.
उपनाम	transfer ensemble, multi-model transfer learning, ensemble of fine-tuned models, ETL	FSL, low-shot learning, k-shot learning, meta-learning for few examples
संबंधित	6	4
सारांश	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.