持续学习的主动学习：在当下保持过去
Active Learning for Continual Learning: Keeping the Past Alive in the Present
AI总结：
针对持续学习中的主动学习，提出基于Fisher信息矩阵累积信息量的AccuACL方法，平衡防遗忘与快速学习，实验显示平均准确率提升23.8%，遗忘降低17.0%。
摘要：
持续学习（CL）使深度神经网络能够适应不断变化的数据分布。在实践中，可能存在标注成本高昂的场景，这导致了主动持续学习（ACL），即在持续学习场景中执行主动学习（AL），当通过选择最具信息量的子集来降低标注成本更为可取时。然而，传统的主动学习策略并不适用于主动持续学习，因为它们仅专注于学习新知识，导致对先前学习任务的灾难性遗忘。因此，主动持续学习需要一种新的主动学习策略，该策略能够平衡防止灾难性遗忘与快速学习新任务的能力。在本文中，我们提出了AccuACL，即基于累积信息量的主动持续学习，通过新颖地使用Fisher信息矩阵作为样本选择的标准，该标准源于对主动持续学习框架内Fisher最优性保持性质的理论分析，同时解决了基于Fisher信息的主动学习的可扩展性问题。大量实验表明，AccuACL在各种持续学习算法上显著优于主动学习基线，平均准确率提高了23.8%，遗忘率降低了17.0%。
英文摘要：
Continual learning (CL) enables deep neural networks to adapt to ever-changing data distributions. In practice, there may be scenarios where annotation is costly, leading to active continual learning (ACL), which performs active learning (AL) for the CL scenarios when reducing the labeling cost by selecting the most informative subset is preferable. However, conventional AL strategies are not suitable for ACL, as they focus solely on learning the new knowledge, leading to catastrophic forgetting of previously learned tasks. Therefore, ACL requires a new AL strategy that can balance the prevention of catastrophic forgetting and the ability to quickly learn new tasks. In this paper, we propose AccuACL, Accumulated informativeness-based Active Continual Learning, by the novel use of the Fisher information matrix as a criterion for sample selection, derived from a theoretical analysis of the Fisher-optimality preservation properties within the framework of ACL, while also addressing the scalability issue of Fisher information-based AL. Extensive experiments demonstrate that AccuACL significantly outperforms AL baselines across various CL algorithms, increasing the average accuracy and forgetting by 23.8% and 17.0%, respectively, on average.
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。
