Paper Detail
Sergi Masip, Alicja Dobrzeniecka, Jonathan Swinnen, Joachim Collin, Bartłomiej Twardowski, Szymon Łukasik, Tinne Tuytelaars
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), a rapidly growing area that lacks a dedicated, systematic review. In this work, we present a comprehensive survey of CSSL for vision, with connections to emerging vision-language settings. First, we analyze existing evaluation protocols and highlight inconsistencies that hinder fair comparison. We then examine why self-supervised objectives exhibit improved robustness to catastrophic forgetting, relating this to task-agnostic representations and smoother loss landscapes. Next, we organize existing methods into a unified taxonomy based on their forgetting-mitigation strategies, including distillation, replay, regularization, architectural approaches, model merging, and objective-level adaptation. Finally, we identify open challenges such as scalability and the need for fast adaptability. We argue that advancing CSSL requires moving beyond small-scale benchmarks towards continual pre-training paradigms for large-scale systems.
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@article{masip2026lifelong,
title = {Lifelong Representations: A Survey on Continual Self-Supervised Learning for Vision Models},
author = {Sergi Masip and Alicja Dobrzeniecka and Jonathan Swinnen and Joachim Collin and Bartłomiej Twardowski and Szymon Łukasik and Tinne Tuytelaars},
year = {2026},
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), a rapidly growing area that lacks a dedicated, systematic review. In this work, we present a comprehensive survey of CSSL for vision, with connections to emerging vision-language settings. First, we analyze existing evaluation},
url = {https://arxiv.org/abs/2607.09785},
keywords = {cs.CV, cs.AI},
eprint = {2607.09785},
archiveprefix = {arXiv},
}
{}