Branch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised Learning

arXiv:2403.18266v1 Announce Type: new
Abstract: Self-supervised learning (SSL) has emerged as an effective paradigm for deriving general representations from vast amounts of unlabeled data. However, as real-world applications continually integrate new content, the high computational and resource demands of SSL necessitate continual learning rather than complete retraining. This poses a challenge in striking a balance between stability and plasticity when adapting to new information. In this paper, we employ Centered Kernel Alignment for quantitatively analyzing model stability and plasticity, revealing …

abstract applications arxiv challenge computational continual cs.cv cs.lg data general however paradigm retraining self-supervised learning ssl stability supervised learning type vast world