用于无监督在线通用持续学习的领域感知数据增强
Domain-Aware Augmentations for Unsupervised Online General Continual Learning
AI总结：
针对无监督在线通用持续学习（UOGCL）的遗忘挑战，本文提出利用依赖流的数据增强及实现技巧优化对比学习的内存使用，该方法简单有效，取得最先进结果并缩小了监督与无监督持续学习的差距。
摘要：
持续学习一直颇具挑战，尤其是在处理无监督在线通用持续学习（UOGCL）等无监督场景时，学习智能体对类别边界或任务变化信息毫无先验知识。尽管先前研究侧重于减少监督设置中的遗忘，但近期研究表明自监督学习者对遗忘更具韧性。本文提出一种新方法，通过定义并使用依赖流的数据增强及部分实现技巧，增强了UOGCL中对比学习的内存使用。所提方法简单有效，在所有考虑的设置中与其他无监督方法相比取得了最先进结果，并缩小了监督与无监督持续学习间的差距。该领域感知增强流程可适配其他基于重放的方法，是持续学习的有效策略。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
