Сравнение методов

	Регуляризованное обучение с малым числом примеров	Перенос обучения
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2016-2020	2010 (formalized); 1990s (early roots)
Автор метода	Multiple (Chen et al., Tian et al., Snell et al., and others)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Тип	Meta-learning framework with explicit regularization	Learning paradigm
Основополагающий источник	Chen, W., Liu, Y., Kira, Z., Wang, Y. F., & Huang, J. (2019). A Closer Look at Few-Shot Classification. International Conference on Learning Representations (ICLR).	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Другие названия	FSL with regularization, regularized meta-learning, few-shot learning with regularization, regularized episodic learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Связанные	5	3
Сводка	Regularized few-shot learning augments standard few-shot learning pipelines with explicit regularization mechanisms — such as weight decay, dropout, data augmentation, label smoothing, or manifold constraints — to reduce overfitting to the tiny support sets that define each episode. This produces more generalizable models when only one to thirty labeled examples per class are available.	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.