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.
|Title of host publication||2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2020 May|
|Event||2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain|
Duration: 2020 May 4 → 2020 May 8
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Conference||2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020|
|Period||20/5/4 → 20/5/8|
Bibliographical noteFunding Information:
This work was supported by Institute for Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) (No. 2019-0-01396, Development of framework for analyzing, detecting, mitigating of bias in AI model and training data)
© 2020 IEEE.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering