A Survey of Unsupervised Deep Domain Adaptation

Authors:
Garrett Wilson

Washington State University Pullman, Pullman, WA, Washington

Washington State University Pullman, Pullman, WA, Washington

0000-0002-6760-754X
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,
Diane J. Cook

Washington State University Pullman, Pullman, WA, Washington

Washington State University Pullman, Pullman, WA, Washington
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ACM Transactions on Intelligent Systems and Technology Volume 11 Issue 5Article No.: 51pp 1–46https://doi.org/10.1145/3400066

Published:05 July 2020Publication History

ACM Transactions on Intelligent Systems and Technology

Abstract

Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not always be the case. As a complement to this challenge, single-source unsupervised domain adaptation can handle situations where a network is trained on labeled data from a source domain and unlabeled data from a related but different target domain with the goal of performing well at test-time on the target domain. Many single-source and typically homogeneous unsupervised deep domain adaptation approaches have thus been developed, combining the powerful, hierarchical representations from deep learning with domain adaptation to reduce reliance on potentially costly target data labels. This survey will compare these approaches by examining alternative methods, the unique and common elements, results, and theoretical insights. We follow this with a look at application areas and open research directions.

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Index Terms

A Survey of Unsupervised Deep Domain Adaptation
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
    2. Machine learning approaches
      1. Neural networks
2. General and reference
  1. Document types
    1. Surveys and overviews

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Published in
ACM Transactions on Intelligent Systems and Technology Volume 11, Issue 5
Survey Paper and Regular Paper
October 2020
325 pages
ISSN:2157-6904
EISSN:2157-6912
DOI:10.1145/3409643
Editor:
Yu Zheng
JD Digits, China
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Publication History
- Published: 5 July 2020
- Accepted: 1 May 2020
- Revised: 1 January 2020
- Received: 1 March 2019
Published in tist Volume 11, Issue 5

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generative adversarial networks
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A Survey of Unsupervised Deep Domain Adaptation

ACM Transactions on Intelligent Systems and Technology

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