Comparar métodos

	Aprendizaje Autosupervisado en Línea	Aprendizaje autosupervisado
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	2020s	2018–2020
Autor original	Multiple contributors (Gidaris, Fini et al., among others)	LeCun, Y. and community (formalized ~2018–2020)
Tipo	Online unsupervised representation learning	Representation learning paradigm
Fuente seminal	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/
Alias	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
Relacionados	3	3
Resumen	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.