  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.
