arXiv cs.CV
· Papers
Lifelong Representations: A Survey on Continual Self-Supervised Learning for Vision Models
arXiv:2607.09785v1 Announce Type: new Abstract: Traditionally, continual learning has assumed access to labeled data, yet many real-world applications -- such as lifelong robotics -- require models to adapt continuously from unlabeled streams. This has led to the development of continual self-supervised learning (CSSL)