  While deep learning (DL) is data-hungry and usually relies on extensive
labeled data to deliver good performance, Active Learning (AL) reduces labeling
costs by selecting a small proportion of samples from unlabeled data for
labeling and training. Therefore, Deep Active Learning (DAL) has risen as a
feasible solution for maximizing model performance under a limited labeling
cost/budget in recent years. Although abundant methods of DAL have been
developed and various literature reviews conducted, the performance evaluation
of DAL methods under fair comparison settings is not yet available. Our work
intends to fill this gap. In this work, We construct a DAL toolkit, DeepAL+, by
re-implementing 19 highly-cited DAL methods. We survey and categorize
DAL-related works and construct comparative experiments across frequently used
datasets and DAL algorithms. Additionally, we explore some factors (e.g., batch
size, number of epochs in the training process) that influence the efficacy of
DAL, which provides better references for researchers to design their DAL
experiments or carry out DAL-related applications.
