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 (CIL), where incoming samples from data stream can be used only once, is more challenging and can be encountered more frequently in real world. Actually, the CIL faces 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 a 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. By utilizing a new weight function, the revised focal loss can pay more attention to current ambiguous samples, which can provide more information of the classification boundary. 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 CIL have shown the effectiveness of AFS. The code will be available at \url{https://github.com/czjghost/AFS}.

翻译：为了模仿人类持续学习的能力，能够从无限数据流中学习的连续学习近期引起了更多关注。在所有场景中，在线类增量学习（CIL）更具挑战性，且在实际中更常见，其中数据流中的传入样本仅能使用一次。实际上，CIL面临稳定性-可塑性困境，其中稳定性指保留旧知识的能力，而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前景，但大多数方法专注于更新和检索记忆的策略，以牺牲可塑性为代价来保持稳定性。为了在稳定性与可塑性之间实现更优权衡，我们提出自适应焦点偏移算法（AFS），该算法在模型学习中动态调整焦点至模糊样本和非目标logits。通过对类别不平衡导致的近因偏差进行深入分析，我们提出一种修正的焦点损失以主要维持稳定性。通过利用新的权重函数，修正的焦点损失能更关注当前模糊样本，从而提供更多分类边界信息。为提升可塑性，我们引入虚拟知识蒸馏。通过设计虚拟教师，该机制将更多注意力分配给非目标类别，可克服过度自信并鼓励模型关注类间信息。在三个流行的CIL数据集上的大量实验证明了AFS的有效性。代码将在\url{https://github.com/czjghost/AFS}公开。