Online class-incremental learning (OCIL) focuses on gradually learning new classes (called plasticity) from a stream of data in a single-pass, while concurrently preserving knowledge of previously learned classes (called stability). The primary challenge in OCIL lies in maintaining a good balance between the knowledge of old and new classes within the continually updated model. Most existing methods rely on explicit knowledge interaction through experience replay, and often employ exclusive training separation to address bias problems. Nevertheless, it still remains a big challenge to achieve a well-balanced learner, as these methods often exhibit either reduced plasticity or limited stability due to difficulties in continually integrating knowledge in the OCIL setting. In this paper, we propose a novel replay-based method, called Balanced Online Incremental Learning (BOIL), which can achieve both high plasticity and stability, thus ensuring more balanced performance in OCIL. Our BOIL method proposes an inclusive training separation strategy using dual classifiers so that knowledge from both old and new classes can effectively be integrated into the model, while introducing implicit approaches for transferring knowledge across the two classifiers. Extensive experimental evaluations over three widely-used OCIL benchmark datasets demonstrate the superiority of BOIL, showing more balanced yet better performance compared to state-of-the-art replay-based OCIL methods.

该研究解决了在线类增量学习（OCIL）中保持旧类和新类知识平衡的挑战。论文提出了一种新的回放方法——平衡在线增量学习（BOIL），采用包容性训练分离策略，利用双分类器实现旧类与新类知识的有效整合，并通过隐式方法增强知识传递。实验结果表明，BOIL在保持高塑性和稳定性的同时，相较于现有的回放方法展现了更优的平衡性能。

平衡在线类增量学习中的包容性训练分离和隐式知识交互