Diversifying Image Synthesis using Data Classification

Yuta Suzuki, Fumihiko Sakaue, Jun Sato

2022

Abstract

In this paper, we propose a method for generating highly diverse images in GAN-based image generation. In recent years, GANs that generate various images such as MSGAN and BicycleGAN have been proposed. By using these methods, it is possible to generate a variety of images to some extent, but when compared with the variety of training images, they are still less diverse. That is, it is still a difficult problem to generate a variety of images, even if a wide variety of training images are being trained. Thus, in this paper, we propose a new structure of GAN which enables us to generate more diversity than the existing methods. Our method estimates the distribution of training images in advance and learns to imitate the diversity of training images. The effectiveness of the proposed method is shown by comparative experiments with the existing methods.

Paper Citation

in Harvard Style

Suzuki Y., Sakaue F. and Sato J. (2022). Diversifying Image Synthesis using Data Classification. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 617-622. DOI: 10.5220/0010902600003124

in Bibtex Style

@conference{visapp22,
author={Yuta Suzuki and Fumihiko Sakaue and Jun Sato},
title={Diversifying Image Synthesis using Data Classification},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={617-622},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010902600003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Diversifying Image Synthesis using Data Classification
SN - 978-989-758-555-5
AU - Suzuki Y.
AU - Sakaue F.
AU - Sato J.
PY - 2022
SP - 617
EP - 622
DO - 10.5220/0010902600003124
PB - SciTePress