Gradient Distribution Alignment Certificates Better Adversarial Domain Adaptation

Zhiqiang Gao, Shufei Zhang, Kaizhu Huang*, Qiufeng Wang, Chaoliang Zhong

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

31 Citations (Scopus)


The latest heuristic for handling the domain shift in unsupervised domain adaptation tasks is to reduce the data distribution discrepancy using adversarial learning. Recent studies improve the conventional adversarial domain adaptation methods with discriminative information by integrating the classifier's outputs into distribution divergence measurement. However, they still suffer from the equilibrium problem of adversarial learning in which even if the discriminator is fully confused, sufficient similarity between two distributions cannot be guaranteed. To overcome this problem, we propose a novel approach named feature gradient distribution alignment (FGDA). We demonstrate the rationale of our method both theoretically and empirically. In particular, we show that the distribution discrepancy can be reduced by constraining feature gradients of two domains to have similar distributions. Meanwhile, our method enjoys a theoretical guarantee that a tighter error upper bound for target samples can be obtained than that of conventional adversarial domain adaptation methods. By integrating the proposed method with existing adversarial domain adaptation models, we achieve state-of-the-art performance on two real-world benchmark datasets.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781665428125
Publication statusPublished - 2021
Event18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 - Virtual, Online, Canada
Duration: 11 Oct 202117 Oct 2021

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499


Conference18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
CityVirtual, Online


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