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Paper Abstract and Keywords
Presentation 2020-03-05 10:45
YuruGAN: Yuru-Charas Generated by Generative Adversarial Networks
Yuki Hagiwara, Toshihisa Tanaka (TUAT) NC2019-93
Abstract (in Japanese) (See Japanese page) 
(in English) Yuru-chara is a mascot character created by local governments and companies for the purpose of publicizing information on areas and products. Since it takes various costs to create just one Yuru-chara, the utilization of Generative Adversarial Networks (GANs) can be expected. In recent years, it has been reported that the use of class conditions in a dataset for GANs training stabilizes learning and improves the quality of generated images. However, it is difficult to apply class conditional GANs when the number of original data is small and a clear class is not given like a Yuru-chara image. In this paper, we propose a class conditional GAN based on clustering and data augmentation. Specifically, first, we performed clustering based on K-means++ on the Yuru-chara image dataset, and converted it to a class conditional dataset. Next, data augmentation was performed on the class conditional dataset so that the number of data was increased five times. In addition, we built a model that incorporates ResBlock, Self-Attention into a network based on class conditional GAN, and trained the
class conditional Yuru-chara dataset. As a result of evaluating the generated images, the effect on the generated images by the
difference of the clustering method was confirmed.
Keyword (in Japanese) (See Japanese page) 
(in English) Generative Adversarial Networks / GANs / Yuru-chara / clustering / Image Generation / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 453, NC2019-93, pp. 101-106, March 2020.
Paper # NC2019-93 
Date of Issue 2020-02-26 (NC) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NC2019-93

Conference Information
Committee NC MBE  
Conference Date 2020-03-04 - 2020-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) University of Electro Communications 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Neuro Computing, Medical Engineering, etc. 
Paper Information
Registration To NC 
Conference Code 2020-03-NC-MBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) YuruGAN: Yuru-Charas Generated by Generative Adversarial Networks 
Sub Title (in English)  
Keyword(1) Generative Adversarial Networks  
Keyword(2) GANs  
Keyword(3) Yuru-chara  
Keyword(4) clustering  
Keyword(5) Image Generation  
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Keyword(7)  
Keyword(8)  
1st Author's Name Yuki Hagiwara  
1st Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
2nd Author's Name Toshihisa Tanaka  
2nd Author's Affiliation Tokyo University of Agriculture and Technology (TUAT)
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Speaker Author-1 
Date Time 2020-03-05 10:45:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2019-93 
Volume (vol) vol.119 
Number (no) no.453 
Page pp.101-106 
#Pages 6 
Date of Issue 2020-02-26 (NC) 

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