Sammenlign metoder

	Regulariseret Few-Shot Læring	Regulariseret Transferlæring
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2016-2020	2000s–2010s
Ophavsperson	Multiple (Chen et al., Tian et al., Snell et al., and others)	Pan, S. J. & Yang, Q. (survey); regularization variants by multiple authors
Type	Meta-learning framework with explicit regularization	Regularized supervised/semi-supervised learning framework
Oprindelig kilde	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.
Aliasser	FSL with regularization, regularized meta-learning, few-shot learning with regularization, regularized episodic learning	regularized domain adaptation, transfer learning with regularization, penalized transfer learning, regularized fine-tuning
Relaterede	5	6
Resumé	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.	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.