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.

在线连续学习（OCL）旨在通过对在线数据流的单次遍历来不断学习新数据，但通常会遭受灾难性遗忘问题。本文对基于回放的两种回放方式进行综合分析，并发现它们可以互补。基于此发现，我们提出了一种名为代理对比回放（PCR）的新型回放方法，该方法通过将锚点 - 样本对替换为锚点 - 代理对来缓解遗忘现象。基于 PCR，我们进一步开发了一种更高级的方法，名为整体代理对比回放（HPCR），它由三个组件组成，在四个数据集上的实验证明了 HPCR 相对于各种现有方法的优越性。