survey

A Survey on Active Deep Learning: From Model Driven to Data Driven

Authors:

Lei ZhaoAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 10s

Article No.: 221, Pages 1 - 34

https://doi.org/10.1145/3510414

Published: 13 September 2022 Publication History

Abstract

Which samples should be labelled in a large dataset is one of the most important problems for the training of deep learning. So far, a variety of active sample selection strategies related to deep learning have been proposed in the literature. We defined them as Active Deep Learning (ADL) only if their predictor or selector is a deep model, where the basic learner is called the predictor and the labeling schemes are called the selector. In this survey, we categorize ADL into model-driven ADL and data-driven ADL by whether its selector is model driven or data driven. We also introduce the different characteristics of the two major types of ADL, respectively. We summarized three fundamental factors in the designation of a selector. We pointed out that, with the development of deep learning, the selector in ADL also is experiencing the stage from model driven to data driven. The advantages and disadvantages between data-driven ADL and model-driven ADL are thoroughly analyzed. Furthermore, different sub-classes of data-drive or model-driven ADL are also summarized and discussed emphatically. Finally, we survey the trend of ADL from model driven to data driven.

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Karaca ABilgin G(2025)An end-to-end active learning framework for limited labelled hyperspectral image classificationInternational Journal of Remote Sensing10.1080/01431161.2025.2467294(1-28)Online publication date: 24-Feb-2025
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Cui YLiu PMa YChen LXu MGuo X(2025)Pansharpening via predictive filtering with element-wise feature mixingISPRS Journal of Photogrammetry and Remote Sensing10.1016/j.isprsjprs.2024.10.029219(22-37)Online publication date: Jan-2025
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Index Terms

A Survey on Active Deep Learning: From Model Driven to Data Driven
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 10s

January 2022

831 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3551649

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Publication History

Published: 13 September 2022

Online AM: 23 March 2022

Accepted: 03 January 2022

Revised: 27 November 2021

Received: 29 May 2021

Published in CSUR Volume 54, Issue 10s

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Cited By

Mousavi SBahaghighat M(2025)Phishing Website Detection: An In‐Depth Investigation of Feature Selection and Deep LearningExpert Systems10.1111/exsy.1382442:3Online publication date: 29-Jan-2025
https://doi.org/10.1111/exsy.13824
Karaca ABilgin G(2025)An end-to-end active learning framework for limited labelled hyperspectral image classificationInternational Journal of Remote Sensing10.1080/01431161.2025.2467294(1-28)Online publication date: 24-Feb-2025
https://doi.org/10.1080/01431161.2025.2467294
Cui YLiu PMa YChen LXu MGuo X(2025)Pansharpening via predictive filtering with element-wise feature mixingISPRS Journal of Photogrammetry and Remote Sensing10.1016/j.isprsjprs.2024.10.029219(22-37)Online publication date: Jan-2025
https://doi.org/10.1016/j.isprsjprs.2024.10.029
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