Acquiring new knowledge without forgetting what has been learned in a sequence of tasks is the central focus of continual learning (CL). While tasks arrive sequentially, the training data are often prepared and annotated independently, leading to the CL of incoming supervised learning tasks. This paper considers the under-explored problem of active continual learning (ACL) for a sequence of active learning (AL) tasks, where each incoming task includes a pool of unlabelled data and an annotation budget. We investigate the effectiveness and interplay between several AL and CL algorithms in the domain, class and task-incremental scenarios. Our experiments reveal the trade-off between two contrasting goals of not forgetting the old knowledge and the ability to quickly learn new knowledge in CL and AL, respectively. While conditioning the AL query strategy on the annotations collected for the previous tasks leads to improved task performance on the domain and task incremental learning, our proposed forgetting-learning profile suggests a gap in balancing the effect of AL and CL for the class-incremental scenario.

翻译：在不遗忘已学知识的前提下获取新知识，是持续学习（CL）的核心研究目标。任务按序到来时，训练数据通常独立准备与标注，从而形成对陆续到来的监督学习任务的持续学习过程。本文探讨了一个尚未充分研究的课题——面向序列化主动学习（AL）任务的主动持续学习（ACL），其中每个新到达的任务包含一批未标注数据及标注预算。我们分析了在领域增量、类别增量和任务增量场景下，多种主动学习与持续学习算法的有效性及其交互作用。实验揭示了持续学习中"不遗忘旧知识"与主动学习中"快速学习新知识"这两个对立目标之间的权衡关系。将主动学习查询策略与先前任务收集的标注信息相结合，能够提升领域增量与任务增量场景下的任务性能；然而我们提出的遗忘-学习曲线表明，在类别增量场景中，主动学习与持续学习效果的平衡仍存在差距。