Sammenlign metoder

	Selv-superviseret få-skuds læring	Overførselslæring
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2019	2010 (formalized); 1990s (early roots)
Ophavsperson	Gidaris, S. et al.; Su, J.-C. et al. (concurrent seminal works)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Type	Hybrid learning paradigm (self-supervised pretraining + few-shot adaptation)	Learning paradigm
Oprindelig kilde	Gidaris, S., Bursuc, A., Komodakis, N., Perez, P., & Cord, M. (2019). Boosting Few-Shot Visual Learning with Self-Supervision. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 8059–8068.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Aliasser	SSL-FSL, self-supervised meta-learning, unsupervised few-shot learning, self-supervised prototypical learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Relaterede	2	3
Resumé	Self-supervised Few-shot Learning (SSL-FSL) combines self-supervised pretraining on large unlabeled corpora with few-shot meta-learning so that a model can recognize new categories from only a handful of labeled examples. By learning rich, transferable representations without expensive annotation, SSL-FSL addresses the fundamental bottleneck of supervised few-shot methods: the need for labeled support data at scale.	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.