方法对比

	自监督迁移学习	自监督少样本学习
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2018–2020 (modern consolidation)	2019
提出者	LeCun, Y. (concept); Devlin et al. (BERT, NLP); Chen et al. (SimCLR, vision)	Gidaris, S. et al.; Su, J.-C. et al. (concurrent seminal works)
类型	Learning paradigm (self-supervised pre-training + fine-tuning)	Hybrid learning paradigm (self-supervised pretraining + few-shot adaptation)
开创性文献	Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 1597–1607.	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.
别名	self-supervised pre-training, SSL-based transfer learning, representation transfer from self-supervised models, contrastive pre-training with transfer	SSL-FSL, self-supervised meta-learning, unsupervised few-shot learning, self-supervised prototypical learning
相关	6	2
摘要	Self-supervised transfer learning combines two powerful paradigms: a model first learns rich representations from unlabeled data using self-supervised pretext tasks, then those learned representations are transferred and fine-tuned on a downstream task with limited labeled data. This approach underlies landmark systems such as BERT in NLP and SimCLR and DINO in computer vision, dramatically reducing labeled-data requirements across many domains.	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.