A Review of Single-Source Deep Unsupervised Visual Domain Adaptation

Sicheng Zhao, Xiangyu Yue, Shanghang Zhang, Bo Li, Han Zhao, Bichen Wu, Ravi Krishna, Joseph E. Gonzalez, Alberto L. Sangiovanni-Vincentelli, Sanjit A. Seshia, Kurt Keutzer

Research output: Contribution to journalArticlepeer-review


Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. However, in many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain. Unfortunately, direct transfer across domains often performs poorly due to the presence of domain shift or dataset bias. Domain adaptation (DA) is a machine learning paradigm that aims to learn a model from a source domain that can perform well on a different (but related) target domain. In this article, we review the latest single-source deep unsupervised DA methods focused on visual tasks and discuss new perspectives for future research. We begin with the definitions of different DA strategies and the descriptions of existing benchmark datasets. We then summarize and compare different categories of single-source unsupervised DA methods, including discrepancy-based methods, adversarial discriminative methods, adversarial generative methods, and self-supervision-based methods. Finally, we discuss future research directions with challenges and possible solutions.
Original languageEnglish (US)
Article number9238468
Pages (from-to)473-493
Number of pages21
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number2
StatePublished - Feb 1 2022
Externally publishedYes


  • Task analysis
  • Data models
  • Adaptation models
  • Visualization
  • Training
  • Loss measurement
  • Learning systems
  • transfer learning
  • discrepancy-based methods
  • self-supervised learning (SSL)
  • Adversarial learning
  • domain adaptation (DA)

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications


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