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Cited by 1,169Machine Learning and AlgorithmsDomain Adaptation and Few-Shot LearningMachine Learning and Data Classification

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A Survey of Deep Active Learning

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Ren, Pengzhen, Xiao, Yun, Chang, Xiaojun, Huang, Po-Yao, Li, Zhihui, Gupta, Brij B., Chen, Xiaojiang, Wang, Xin·2022·ACM Computing Surveys
DOI 10.1145/3472291
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Abstract

Active learning (AL) attempts to maximize a model’s performance gain while annotating the fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize a massive number of parameters if the model is to learn how to extract high-quality features. In recent years, due to the rapid development of internet technology, we have entered an era of information abundance characterized by massive amounts of available data. As a result, DL has attracted significant attention from researchers and has been rapidly developed. Compared with DL, however, researchers have a relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples, meaning that early AL is rarely according the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to a large number of publicly available annotated datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, making it unfeasible in fields that require high levels of expertise (such as speech recognition, information extraction, medical images, etc.). Therefore, AL is gradually coming to receive the attention it is due. It is therefore natural to investigate whether AL can be used to reduce the cost of sample annotation while retaining the powerful learning capabilities of DL. As a result of such investigations, deep active learning (DeepAL) has emerged. Although research on this topic is quite abundant, there has not yet been a comprehensive survey of DeepAL-related works; accordingly, this article aims to fill this gap. We provide a formal classification method for the existing work, along with a comprehensive and systematic overview. In addition, we also analyze and summarize the development of DeepAL from an application perspective. Finally, we discuss the confusion and problems associated with DeepAL and provide some possible development directions.

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Metadata

Authors
Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Gupta, B. B., Chen, X., & Wang, X.
Affiliations
  • Northwest University (CN)
  • RMIT University (AU)
  • Carnegie Mellon University (US)
  • Qilu University of Technology (CN)
  • National Institute of Technology Kurukshetra (IN)
ORCID
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2021-10-08
Volume
54
Issue
9
Pages
1-40
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3472291
OpenAlex
W3205626500
Cited by
1,169
References
152
Citations by year
2026: 143 · 2025: 256 · 2024: 297 · 2023: 302 · 2022: 125 · 2021: 43
FWCI
68.03
Type
journal article
Language
en
Open access
Licence
Locations
  • ACM Computing Surveys (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • RMIT Research Repository (RMIT University Library) (submittedVersion)
  • Figshare (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
  • NSFC (61972315 and 62072372)
  • Shaanxi Science and Technology Innovation Team Support (2018TD-026)
  • Australian Research Council Discovery Early Career Researcher Award (DE190100626)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Keywords
deep active learning, active learning (machine learning), deep learning, annotation cost reduction

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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