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

Understand

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 the critical roles of batch normalization layers for stability and convolutional layers for plasticity.