A Survey of Deep Active Learning

by   Pengzhen Ren, et al.

Active learning (AL) attempts to maximize the performance gain of the model by marking the fewest samples. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters, so that the model learns how to extract high-quality features. In recent years, due to the rapid development of internet technology, we are in an era of information torrents and we have massive amounts of data. In this way, DL has aroused strong interest of researchers and has been rapidly developed. Compared with DL, researchers have relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples. Therefore, early AL is difficult to reflect the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to the publicity of the large number of existing annotation datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, which is not allowed in some fields that require high expertise, especially in the fields of speech recognition, information extraction, medical images, etc. Therefore, AL has gradually received due attention. A natural idea is whether AL can be used to reduce the cost of sample annotations, while retaining the powerful learning capabilities of DL. Therefore, deep active learning (DAL) has emerged. Although the related research has been quite abundant, it lacks a comprehensive survey of DAL. This article is to fill this gap, we provide a formal classification method for the existing work, and a comprehensive and systematic overview. In addition, we also analyzed and summarized the development of DAL from the perspective of application. Finally, we discussed the confusion and problems in DAL, and gave some possible development directions for DAL.


A Comparative Survey of Deep Active Learning

Active Learning (AL) is a set of techniques for reducing labeling cost b...

Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort

Large amounts of annotated data have become more important than ever, es...

Active Learning for Event Extraction with Memory-based Loss Prediction Model

Event extraction (EE) plays an important role in many industrial applica...

Hyperspectral Image Classification – Traditional to Deep Models: A Survey for Future Prospects

Hyperspectral Imaging (HSI) has been extensively utilized in many real-l...

A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples

With the rapid development of deep learning technology and improvement i...

TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks

Deep learning (DL) has achieved unprecedented success in a variety of ta...

Code Repositories



view repo


This is a paper list of suggestive annotation direction.

view repo