  Rehearsal is a critical component for class-incremental continual learning,
yet it requires a substantial memory budget. Our work investigates whether we
can significantly reduce this memory budget by leveraging unlabeled data from
an agent's environment in a realistic and challenging continual learning
paradigm. Specifically, we explore and formalize a novel semi-supervised
continual learning (SSCL) setting, where labeled data is scarce yet non-i.i.d.
unlabeled data from the agent's environment is plentiful. Importantly, data
distributions in the SSCL setting are realistic and therefore reflect object
class correlations between, and among, the labeled and unlabeled data
distributions. We show that a strategy built on pseudo-labeling, consistency
regularization, Out-of-Distribution (OoD) detection, and knowledge distillation
reduces forgetting in this setting. Our approach, DistillMatch, increases
performance over the state-of-the-art by no less than 8.7% average task
accuracy and up to a 54.5% increase in average task accuracy in SSCL CIFAR-100
experiments. Moreover, we demonstrate that DistillMatch can save up to 0.23
stored images per processed unlabeled image compared to the next best method
which only saves 0.08. Our results suggest that focusing on realistic
correlated distributions is a significantly new perspective, which accentuates
the importance of leveraging the world's structure as a continual learning
strategy.
