Continual Recognition with Adaptive Memory Update

Class-incremental continual learning aims to improve the ability of modern classification models to continually recognize new classes without forgetting the previous ones. Prior art in the field has largely considered using a replay buffer. In this paper, we start from a observation that existing replay based method would fail when the stored exemplars are not hard enough to get a good decision boundary between previously learned class and new class. To prevent this situation, we propose a method from the perspective of remedy after forgetting for the first time. In the proposed method, a set of exemplars is preserved as a working memory, which helps recognize new classes. When the working memory is insufficient to distinguish between new classes, more discriminating samples would be swapped from a long-term memory, which is built up during the early training process, into working memory in an adaptive way. Our continual recognition model with adaptive memory update is capable of overcoming the problem of catastrophic forgetting with various new classes coming in sequence, especially for similar but different classes. Extensive experiments on different real-world datasets demonstrate that the proposed model is superior to existing state-of-the-art algorithms. Moreover, our model can be used as a general plugin for any replay-based continual learning algorithm to further improve their performance.