SemiStarGAN: Semi-supervised Generative Adversarial Networks for Multi-domain Image-to-Image Translation

Shu Yu Hsu, Chih Yuan Yang, Chi Chia Huang, Jane Yung jen Hsu*

*Corresponding author for this work

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

3 Scopus citations


Recent studies have shown significant advance for multi-domain image-to-image translation, and generative adversarial networks (GANs) are widely used to address this problem. However, to train an effective image generator, existing methods all require a large number of domain-labeled images, which may take time and effort to collect for real-world problems. In this paper, we propose SemiStarGAN, a semi-supervised GAN network to tackle this issue. The proposed method utilizes unlabeled images by incorporating a novel discriminator/classifier network architecture—Y model, and two existing semi-supervised learning techniques—pseudo labeling and self-ensembling. Experimental results on the CelebA dataset using domains of facial attributes show that the proposed method achieves comparable performance with state-of-the-art methods using considerably less labeled training images.

Original languageEnglish
Title of host publicationComputer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers
EditorsHongdong Li, Greg Mori, C.V. Jawahar, Konrad Schindler
PublisherSpringer Verlag
Number of pages16
ISBN (Print)9783030208691
StatePublished - 2019
Externally publishedYes
Event14th Asian Conference on Computer Vision, ACCV 2018 - Perth, Australia
Duration: 02 12 201806 12 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11364 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th Asian Conference on Computer Vision, ACCV 2018

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.


  • Generative adversarial network
  • Image-to-image translation
  • Semi-supervised learning


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