  Online class-incremental continual learning is a specific task of continual
learning. It aims to continuously learn new classes from data stream and the
samples of data stream are seen only once, which suffers from the catastrophic
forgetting issue, i.e., forgetting historical knowledge of old classes.
Existing replay-based methods effectively alleviate this issue by saving and
replaying part of old data in a proxy-based or contrastive-based replay manner.
Although these two replay manners are effective, the former would incline to
new classes due to class imbalance issues, and the latter is unstable and hard
to converge because of the limited number of samples. In this paper, we conduct
a comprehensive analysis of these two replay manners and find that they can be
complementary. Inspired by this finding, we propose a novel replay-based method
called proxy-based contrastive replay (PCR). The key operation is to replace
the contrastive samples of anchors with corresponding proxies in the
contrastive-based way. It alleviates the phenomenon of catastrophic forgetting
by effectively addressing the imbalance issue, as well as keeps a faster
convergence of the model. We conduct extensive experiments on three real-world
benchmark datasets, and empirical results consistently demonstrate the
superiority of PCR over various state-of-the-art methods.
