  We study Online Continual Learning with missing labels and propose SemiCon, a
new contrastive loss designed for partly labeled data. We demonstrate its
efficiency by devising a memory-based method trained on an unlabeled data
stream, where every data added to memory is labeled using an oracle. Our
approach outperforms existing semi-supervised methods when few labels are
available, and obtain similar results to state-of-the-art supervised methods
while using only 2.6% of labels on Split-CIFAR10 and 10% of labels on
Split-CIFAR100.
