Online continual learning (OCL) aims to continuously learn new data from a single pass over the online data stream. It generally suffers from the catastrophic forgetting issue. Existing replay-based methods effectively alleviate this issue by replaying part of old data in a proxy-based or contrastive-based replay manner. In this paper, we conduct a comprehensive analysis of these two replay manners and find they can be complementary. Inspired by this finding, we propose a novel replay-based method called proxy-based contrastive replay (PCR), which replaces anchor-to-sample pairs with anchor-to-proxy pairs in the contrastive-based loss to alleviate the phenomenon of forgetting. Based on PCR, we further develop a more advanced method named holistic proxy-based contrastive replay (HPCR), which consists of three components. The contrastive component conditionally incorporates anchor-to-sample pairs to PCR, learning more fine-grained semantic information with a large training batch. The second is a temperature component that decouples the temperature coefficient into two parts based on their impacts on the gradient and sets different values for them to learn more novel knowledge. The third is a distillation component that constrains the learning process to keep more historical knowledge. Experiments on four datasets consistently demonstrate the superiority of HPCR over various state-of-the-art methods.

翻译：在线持续学习旨在通过单次处理在线数据流来持续学习新数据，通常面临灾难性遗忘问题。现有基于回放的方法通过代理回放或对比回放方式重放部分旧数据，有效缓解了该问题。本文深入分析这两种回放方式后发现它们具有互补性。基于这一发现，我们提出一种名为代理对比回放（PCR）的新型回放方法，该方法在对比损失函数中用锚点-代理对替代锚点-样本对，以缓解遗忘现象。在PCR基础上，我们进一步开发了更先进的方法——整体代理对比回放（HPCR），该方法包含三个组件：对比组件有条件地将锚点-样本对融入PCR，通过大训练批次学习更细粒度的语义信息；温度组件将温度系数按梯度影响解耦为两部分，并设置不同数值以学习更多新知识；蒸馏组件约束学习过程以保留更多历史知识。在四个数据集上的实验结果一致表明，HPCR相对于多种最新方法具有显著优越性。