Continual learning strives to ensure stability in solving previously seen tasks while demonstrating plasticity in a novel domain. Recent advances in CL are mostly confined to a supervised learning setting, especially in NLP domain. In this work, we consider a few-shot continual active learning (CAL) setting where labeled data is inadequate, and unlabeled data is abundant but with a limited annotation budget. We propose a simple but efficient method, called Meta-Continual Active Learning. Specifically, we employ meta-learning and experience replay to address the trade-off between stability and plasticity. As a result, it finds an optimal initialization that efficiently utilizes annotated information for fast adaptation while preventing catastrophic forgetting of past tasks. We conduct extensive experiments to validate the effectiveness of the proposed method and analyze the effect of various active learning strategies and memory sample selection methods in a few-shot CAL setup. Our experiment results demonstrate that random sampling is the best default strategy for both active learning and memory sample selection to solve few-shot CAL problems.

翻译：持续学习致力于在解决已知任务时保持稳定性，同时在新领域展现可塑性。近期持续学习领域的进展主要局限于监督学习设置，尤其是在自然语言处理领域。本研究考虑了一种少样本持续主动学习（CAL）设置，其中标记数据不足，未标记数据丰富但标注预算有限。我们提出了一种简单而高效的方法，称为元持续主动学习。具体而言，我们采用元学习和经验回放来应对稳定性与可塑性之间的权衡。该方法能够找到最优初始化，有效利用标注信息实现快速适应，同时防止对过往任务的灾难性遗忘。我们通过大量实验验证了所提方法的有效性，并分析了在少样本CAL设置下不同主动学习策略和记忆样本选择方法的效果。实验结果表明，随机采样是解决少样本CAL问题时主动学习和记忆样本选择的最佳默认策略。