Continual Learning has been challenging, especially when dealing with
unsupervised scenarios such as Unsupervised Online General Continual Learning
(UOGCL), where the learning agent has no prior knowledge of class boundaries or
task change information. While previous research has focused on reducing
forgetting in supervised setups, recent studies have shown that self-supervised
learners are more resilient to forgetting. This paper proposes a novel approach
that enhances memory usage for contrastive learning in UOGCL by defining and
using stream-dependent data augmentations together with some implementation
tricks. Our proposed method is simple yet effective, achieves state-of-the-art
results compared to other unsupervised approaches in all considered setups, and
reduces the gap between supervised and unsupervised continual learning. Our
domain-aware augmentation procedure can be adapted to other replay-based
methods, making it a promising strategy for continual learning.

这篇论文提出了一种针对无监督在线普通持续学习的对比学习增强内存使用的新方法，通过定义和使用流依赖的数据增强及实现技巧，提升了持续学习中的遗忘问题，实现了与有监督方法相媲美的最新结果，为持续学习提供了有希望的策略。