Active learning seeks to achieve strong performance with fewer training samples. It does this by iteratively asking an oracle to label new selected samples in a human-in-the-loop manner. This technique has gained increasing popularity due to its broad applicability, yet its survey papers, especially for deep learning-based active learning (DAL), remain scarce. Therefore, we conduct an advanced and comprehensive survey on DAL. We first introduce reviewed paper collection and filtering. Second, we formally define the DAL task and summarize the most influential baselines and widely used datasets. Third, we systematically provide a taxonomy of DAL methods from five perspectives, including annotation types, query strategies, deep model architectures, learning paradigms, and training processes, and objectively analyze their strengths and weaknesses. Then, we comprehensively summarize main applications of DAL in Natural Language Processing (NLP), Computer Vision (CV), and Data Mining (DM), etc. Finally, we discuss challenges and perspectives after a detailed analysis of current studies. This work aims to serve as a useful and quick guide for researchers in overcoming difficulties in DAL. We hope that this survey will spur further progress in this burgeoning field.

深度学习主导的主动学习方法的调查；介绍了 DAL 任务，总结了最重要的基准和常用的数据集；系统地提供了 DAL 方法的分类，包括注释类型、查询策略、深度模型架构、学习范式和训练过程，并客观分析它们的优点和缺点；全面总结了 DAL 在自然语言处理、计算机视觉和数据挖掘等领域的主要应用；在对当前研究进行详细分析后，讨论了挑战和展望。

深度主动学习综述：近期进展与新前沿