Effective Visual Domain Adaptation via Generative Adversarial Distribution Matching

Qi Kang, Siya Yao, Mengchu Zhou, Kai Zhang, Abdullah Abusorrah

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


In the field of computer vision, without sufficient labeled images, it is challenging to train an accurate model. However, through visual adaptation from source to target domains, a relevant labeled dataset can help solve such problem. Many methods apply adversarial learning to diminish cross-domain distribution difference. They are able to greatly enhance the performance on target classification tasks. Generative adversarial network (GAN) loss is widely used in adversarial adaptation learning methods to reduce an across-domain distribution difference. However, it becomes difficult to decline such distribution difference if generator or discriminator in GAN fails to work as expected and degrades its performance. To solve such cross-domain classification problems, we put forward a novel adaptation framework called generative adversarial distribution matching (GADM). In GADM, we improve the objective function by taking cross-domain discrepancy distance into consideration and further minimize the difference through the competition between a generator and discriminator, thereby greatly decreasing cross-domain distribution difference. Experimental results and comparison with several state-of-the-art methods verify GADM's superiority in image classification across domains.

Original languageEnglish (US)
Article number9194389
Pages (from-to)3919-3929
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number9
StatePublished - Sep 2021

All Science Journal Classification (ASJC) codes

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


  • Adversarial learning
  • distribution matching
  • generative adversarial networks (GANs)
  • image classification
  • visual domain adaptation


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