Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation

Xinpeng Xie, Jiawei Chen, Yuexiang Li, Linlin Shen, Kai Ma, Yefeng Zheng

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)


Recent generative adversarial network (GAN) based methods (e.g., CycleGAN) are prone to fail at preserving image-objects in image-to-image translation, which reduces their practicality on tasks such as domain adaptation. Some frameworks have been proposed to adopt a segmentation network as the auxiliary regularization to prevent the content distortion. However, all of them require extra pixel-wise annotations, which is difficult to fulfill in practical applications. In this paper, we propose a novel GAN (namely OP-GAN) to address the problem, which involves a self-supervised module to enforce the image content consistency during image-to-image translations without any extra annotations. We evaluate the proposed OP-GAN on three publicly available datasets. The experimental results demonstrate that our OP-GAN can yield visually plausible translated images and significantly improve the semantic segmentation accuracy in different domain adaptation scenarios with off-the-shelf deep learning networks such as PSPNet and U-Net.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings
EditorsAndrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783030585648
Publication statusPublished - 2020
Externally publishedYes
Event16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom
Duration: 23 Aug 202028 Aug 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12365 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th European Conference on Computer Vision, ECCV 2020
Country/TerritoryUnited Kingdom


  • Domain adaptation
  • Image-to-image translation
  • Semantic segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


Dive into the research topics of 'Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation'. Together they form a unique fingerprint.

Cite this