Comparer des méthodes

	Apprentissage par transfert régularisé	Apprentissage à peu d'exemples
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000s–2010s	2011–2017
Auteur d'origine	Pan, S. J. & Yang, Q. (survey); regularization variants by multiple authors	Lake, B. M.; Vinyals, O.; Finn, C. et al.
Type	Regularized supervised/semi-supervised learning framework	Meta-learning / low-data learning paradigm
Source fondatrice	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.
Alias	regularized domain adaptation, transfer learning with regularization, penalized transfer learning, regularized fine-tuning	FSL, low-shot learning, k-shot learning, meta-learning for few examples
Apparentées	6	4
Résumé	Regularized Transfer Learning applies explicit penalty terms to a transfer learning pipeline to control how much a model shifts away from source-domain knowledge when adapting to a new target domain. The regularizer discourages negative transfer — the harmful carry-over of irrelevant source patterns — while preserving beneficial shared representations and preventing overfitting when target-domain labels are scarce.	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.