  Targeted for real world scenarios, online continual learning aims to learn
new tasks from sequentially available data under the condition that each data
is observed only once by the learner. Though recent works have made remarkable
achievements by storing part of learned task data as exemplars for knowledge
replay, the performance is greatly relied on the size of stored exemplars while
the storage consumption is a significant constraint in continual learning. In
addition, storing exemplars may not always be feasible for certain applications
due to privacy concerns. In this work, we propose a novel exemplar-free method
by leveraging nearest-class-mean (NCM) classifier where the class mean is
estimated during training phase on all data seen so far through online mean
update criteria. We focus on image classification task and conduct extensive
experiments on benchmark datasets including CIFAR-100 and Food-1k. The results
demonstrate that our method without using any exemplar outperforms
state-of-the-art exemplar-based approaches with large margins under standard
protocol (20 exemplars per class) and is able to achieve competitive
performance even with larger exemplar size (100 exemplars per class).
