  We introduce a novel Pseudo-Negative Regularization (PNR) framework for
effective continual self-supervised learning (CSSL). Our PNR leverages
pseudo-negatives obtained through model-based augmentation in a way that newly
learned representations may not contradict what has been learned in the past.
Specifically, for the InfoNCE-based contrastive learning methods, we define
symmetric pseudo-negatives obtained from current and previous models and use
them in both main and regularization loss terms. Furthermore, we extend this
idea to non-contrastive learning methods which do not inherently rely on
negatives. For these methods, a pseudo-negative is defined as the output from
the previous model for a differently augmented version of the anchor sample and
is asymmetrically applied to the regularization term. Extensive experimental
results demonstrate that our PNR framework achieves state-of-the-art
performance in representation learning during CSSL by effectively balancing the
trade-off between plasticity and stability.
