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.

翻译：持续学习一直具有挑战性，尤其在处理无监督场景（如无监督在线通用持续学习，UOGCL）时，学习主体无法预先了解类别边界或任务变化信息。尽管以往研究主要关注减少监督设置下的遗忘，但近期研究表明自监督学习器对遗忘更具鲁棒性。本文提出了一种新颖方法，通过定义并利用流依赖数据增强技术及一些实现技巧，增强了UOGCL中对比学习的内存利用率。所提方法简洁而高效，在所有考虑设置中均取得了优于其他无监督方法的最新成果，并缩小了监督与无监督持续学习之间的差距。我们的域感知增强流程可适配于其他基于回放的方法，因此成为持续学习中一种有前景的策略。