research-article

Deep visual domain adaptation: : A survey

Authors:

Weihong DengAuthors Info & Claims

Volume 312, Issue C

Pages 135 - 153

https://doi.org/10.1016/j.neucom.2018.05.083

Published: 27 October 2018 Publication History

Abstract

Deep domain adaptation has emerged as a new learning technique to address the lack of massive amounts of labeled data. Compared to conventional methods, which learn shared feature subspaces or reuse important source instances with shallow representations, deep domain adaptation methods leverage deep networks to learn more transferable representations by embedding domain adaptation in the pipeline of deep learning. There have been comprehensive surveys for shallow domain adaptation, but few timely reviews the emerging deep learning based methods. In this paper, we provide a comprehensive survey of deep domain adaptation methods for computer vision applications with four major contributions. First, we present a taxonomy of different deep domain adaptation scenarios according to the properties of data that define how two domains are diverged. Second, we summarize deep domain adaptation approaches into several categories based on training loss, and analyze and compare briefly the state-of-the-art methods under these categories. Third, we overview the computer vision applications that go beyond image classification, such as face recognition, semantic segmentation and object detection. Fourth, some potential deficiencies of current methods and several future directions are highlighted.

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Index Terms

Deep visual domain adaptation: A survey
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Machine learning
    1. Learning paradigms
    2. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Neurocomputing

Neurocomputing Volume 312, Issue C

Oct 2018

390 pages

ISSN:0925-2312

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Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 27 October 2018

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Li YYan YWang X(2024)Post-disaster building damage detection using multi-source variational domain adaptationJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23261346:1(389-404)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JIFS-232613
Dong YChe XFu YLiu HSun L(2024)Classification of human protein cell images using deep neural networksJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22346446:2(4159-4172)Online publication date: 14-Feb-2024
https://dl.acm.org/doi/10.3233/JIFS-223464
Farhadloo MSharma AShekhar SMarkovic S(2024)Spatial Computing Opportunities in Biomedical Decision Support: The Atlas-EHR VisionACM Transactions on Spatial Algorithms and Systems10.1145/367920110:3(1-36)Online publication date: 23-Jul-2024
https://dl.acm.org/doi/10.1145/3679201
Xu YCao HXie LLi XChen ZYang J(2024)Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive SurveyACM Computing Surveys10.1145/367901056:12(1-36)Online publication date: 22-Jul-2024
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Gharoun HMomenifar FChen FGandomi A(2024)Meta-learning Approaches for Few-Shot Learning: A Survey of Recent AdvancesACM Computing Surveys10.1145/365994356:12(1-41)Online publication date: 25-Jul-2024
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Zhu RGuo DQi DChu ZYu XLi S(2024)A Survey of Trustworthy Representation Learning Across DomainsACM Transactions on Knowledge Discovery from Data10.1145/365730118:7(1-53)Online publication date: 12-Apr-2024
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Pai YSun NLi CLin S(2024)Incremental Data Drifting: Evaluation Metrics, Data Generation, and Approach ComparisonACM Transactions on Intelligent Systems and Technology10.1145/365563015:4(1-26)Online publication date: 24-May-2024
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Zhang JLiu XZhang YZhu GNiu JTang SBaeza-Yates RBonchi F(2024)Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671908(4191-4202)Online publication date: 25-Aug-2024
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Zirak AHemmati H(2024)Improving Automated Program Repair with Domain AdaptationACM Transactions on Software Engineering and Methodology10.1145/363197233:3(1-43)Online publication date: 15-Mar-2024
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