विधियों की तुलना करें

	एन्सेम्बल फ्यू-शॉट लर्निंग (Ensemble Few-Shot Learning)	ट्रांसफर लर्निंग
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2019	2010 (formalized); 1990s (early roots)
प्रवर्तक	Dvornik, N., Schmid, C., & Mairal, J.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
प्रकार	Ensemble of few-shot learners	Learning paradigm
मौलिक स्रोत	Dvornik, N., Schmid, C., & Mairal, J. (2019). Diversity with Cooperation: Ensemble Methods for Few-Shot Classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3716–3725.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
उपनाम	ensemble few-shot classification, multi-model few-shot learning, few-shot ensemble, cooperative few-shot ensemble	TL, domain adaptation, fine-tuning, pre-trained model adaptation
संबंधित	5	3
सारांश	Ensemble Few-Shot Learning combines multiple few-shot models — such as prototypical networks or embedding learners — to classify new classes from only one to a handful of labeled examples. By enforcing diversity among base learners and aggregating their predictions, the ensemble consistently outperforms any single few-shot model in accuracy and robustness, especially under severe label scarcity.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.