Unsupervised Image-to-Image Translation Via Fair Representation of Gender Bias

Sunhee Hwang, Hyeran Byun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Fairness becomes a critical issue of computer vision to reduce discriminative factors in various systems. Among computer vision tasks, Image-to-Image translation for facial attributes editing can yield discriminative results. The unexpected gender changed results can be generated instead of editing target attributes due to the dataset imbalance problem. In this work, we propose a framework of unsupervised Image-to-Image translation that learns a fair representation by separating the latent space of our model into two purposes: 1) Target Attribute Editing, 2) Gender Preserving. We evaluate the proposed framework on CelebA dataset. Both quantitive and qualitative results demonstrate that our method improves image quality and fairness than the prior Image-to-Image translation method.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1953-1957
Number of pages5
ISBN (Electronic)9781509066315
DOIs
Publication statusPublished - 2020 May
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: 2020 May 42020 May 8

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Country/TerritorySpain
CityBarcelona
Period20/5/420/5/8

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Unsupervised Image-to-Image Translation Via Fair Representation of Gender Bias'. Together they form a unique fingerprint.

Cite this