Sammenlign metoder

	Online Self-supervised Learning	Online læring
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2020s	1958–2000s
Ophavsperson	Multiple contributors (Gidaris, Fini et al., among others)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Type	Online unsupervised representation learning	Learning paradigm (sequential model update)
Oprindelig kilde	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.
Aliasser	online SSL, continual self-supervised learning, streaming self-supervised learning, incremental self-supervised learning	incremental learning, sequential learning, streaming learning, online machine learning
Relaterede	3	6
Resumé	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.