Сравнение методов

	Онлайн-самообучение без учителя	Онлайн-обучение	Перенос обучения
Область	Машинное обучение	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning	Machine learning
Год появления	2020s	1958–2000s	2010 (formalized); 1990s (early roots)
Автор метода	Multiple contributors (Gidaris, Fini et al., among others)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Тип	Online unsupervised representation learning	Learning paradigm (sequential model update)	Learning paradigm
Основополагающий источник	Gidaris, S., Bursuc, A., Komodakis, N., Perez, P., & Cord, M. (2021). OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 6830–6840.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Другие названия	online SSL, continual self-supervised learning, streaming self-supervised learning, incremental self-supervised learning	incremental learning, sequential learning, streaming learning, online machine learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Связанные	3	6	3
Сводка	Online Self-supervised Learning (online SSL) trains neural networks on unlabeled data that arrives sequentially or in streams, using automatically generated supervisory signals (pretext tasks) instead of human labels. By updating the model continuously as new data flows in, it enables perpetually evolving representations without storing the full dataset — critical for real-time systems, edge devices, and privacy-constrained settings.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.	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.