Sammenlign metoder

	Online Self-supervised Learning	Selvovervåget læring	Overførselslæring
Fagområde	Maskinlæring	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning	Machine learning
Oprindelsesår	2020s	2018–2020	2010 (formalized); 1990s (early roots)
Ophavsperson	Multiple contributors (Gidaris, Fini et al., among others)	LeCun, Y. and community (formalized ~2018–2020)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Type	Online unsupervised representation learning	Representation learning paradigm	Learning paradigm
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.	LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Aliasser	online SSL, continual self-supervised learning, streaming self-supervised learning, incremental self-supervised learning	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Relaterede	3	3	3
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.	Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.	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.