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Authors: Hiroki Adachi ; Hiroshi Fukui ; Takayoshi Yamashita and Hironobu Fujiyoshi

Affiliation: Chubu University, Kasugai, Aichi and Japan

ISBN: 978-989-758-354-4

Keyword(s): Conditional Generative Adversarial Networks, CelebA Dataset.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image Formation and Preprocessing ; Image Generation Pipeline: Algorithms and Techniques

Abstract: CGANs are generative models that depend on Deep Learning and can generate images that meet given conditions. However, if a network has a deep architecture, conditions do not provide enough information, so unnatural images are generated. In this paper, we propose a facial image generation method by introducing weighted conditions to CGANs. Weighted condition vectors are input in each layer of a generator, and then a discriminator is extend to multi-tasks so as to recognize input conditions. This approach can step-by-step reflect conditions inputted to the generator at every layer, fulfill the input conditions, and generate high quality images. We demonstrate the effectiveness of our method in both subjective and objective evaluation experiments.

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Paper citation in several formats:
Adachi, H.; Fukui, H.; Yamashita, T. and Fujiyoshi, H. (2019). Facial Image Generation by Generative Adversarial Networks using Weighted Conditions.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP, ISBN 978-989-758-354-4, pages 139-145. DOI: 10.5220/0007377601390145

@conference{visapp19,
author={Hiroki Adachi. and Hiroshi Fukui. and Takayoshi Yamashita. and Hironobu Fujiyoshi.},
title={Facial Image Generation by Generative Adversarial Networks using Weighted Conditions},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP,},
year={2019},
pages={139-145},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007377601390145},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP,
TI - Facial Image Generation by Generative Adversarial Networks using Weighted Conditions
SN - 978-989-758-354-4
AU - Adachi, H.
AU - Fukui, H.
AU - Yamashita, T.
AU - Fujiyoshi, H.
PY - 2019
SP - 139
EP - 145
DO - 10.5220/0007377601390145

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