Methoden vergleichen

	Transfer Learning	Few-Shot Learning
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2010 (formalized); 1990s (early roots)	2011–2017
Urheber	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)	Lake, B. M.; Vinyals, O.; Finn, C. et al.
Typ	Learning paradigm	Meta-learning / low-data learning paradigm
Wegweisende Quelle	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	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.
Aliasnamen	TL, domain adaptation, fine-tuning, pre-trained model adaptation	FSL, low-shot learning, k-shot learning, meta-learning for few examples
Verwandt	3	4
Zusammenfassung	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.	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.