To imitate the ability of keeping learning of human, continual learning which can learn from a never-ending data stream has attracted more interests recently. In all settings, the online class incremental learning (OCIL), where incoming samples from data stream can be used only once, is more challenging and can be encountered more frequently in real world. Actually, all continual learning models face a stability-plasticity dilemma, where the stability means the ability to preserve old knowledge while the plasticity denotes the ability to incorporate new knowledge. Although replay-based methods have shown exceptional promise, most of them concentrate on the strategy for updating and retrieving memory to keep stability at the expense of plasticity. To strike a preferable trade-off between stability and plasticity, we propose an Adaptive Focus Shifting algorithm (AFS), which dynamically adjusts focus to ambiguous samples and non-target logits in model learning. Through a deep analysis of the task-recency bias caused by class imbalance, we propose a revised focal loss to mainly keep stability. \Rt{By utilizing a new weight function, the revised focal loss will pay more attention to current ambiguous samples, which are the potentially valuable samples to make model progress quickly.} To promote plasticity, we introduce a virtual knowledge distillation. By designing a virtual teacher, it assigns more attention to non-target classes, which can surmount overconfidence and encourage model to focus on inter-class information. Extensive experiments on three popular datasets for OCIL have shown the effectiveness of AFS. The code will be available at \url{https://github.com/czjghost/AFS}.

翻译：为模仿人类持续学习的能力，能够从无休止的数据流中学习的持续学习近期引起了更多关注。在所有设定中，在线类别增量学习（OCIL）更具挑战性，且在实际场景中更为常见——数据流中的新样本仅能被使用一次。实际上，所有持续学习模型都面临稳定性-可塑性困境：稳定性指保留旧知识的能力，可塑性指整合新知识的能力。尽管基于回放的方法展现出卓越潜力，但大多侧重于通过更新和检索记忆的策略来保持稳定性，却牺牲了可塑性。为在稳定性与可塑性间取得更优平衡，我们提出自适应焦点转移算法（AFS），该算法在模型学习中动态调整对模糊样本和非目标逻辑值的关注度。通过深入分析类别不平衡导致的任务近因偏差，我们提出改进型焦点损失函数以主要维持稳定性。通过采用新型权重函数，改进型焦点损失将更关注当前模糊样本——这些潜在有价值的样本能加速模型进步。为提升可塑性，我们引入虚拟知识蒸馏。通过设计虚拟教师，该方法将更多注意力分配给非目标类别，既能克服过度自信问题，又可促使模型聚焦于类间信息。在三个主流OCIL数据集上的大量实验验证了AFS的有效性。代码将在\url{https://github.com/czjghost/AFS}开放。