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Paper Abstract and Keywords
Presentation 2021-02-19 14:15
[Special Talk] A Note on Electron Microscope Image Generation from Mix Proportion via Conditional Style Generative Adversarial Network for Rubber Materials
Rintaro Yanagi, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) Estimating the properties of rubber materials from ingredients is necessary to accelerate rubber material development. In conventional methods, they synthesize rubber materials from ingredients, and then they collect pairs of ingredient mix proportions and rubber properties via various evaluation tests. By utilizing these pairs as training data, they realize rubber property estimation from unknown ingredient mix proportions. However, conducting the evaluation tests takes a lot of costs and then it is difficult to flexibly apply these methods for new ingredients. On the other hand, it is well known that rubber materials with similar properties possess similar electron microscope images. Therefore, image generation utilizing pairs of ingredient mix proportions and electron microscope images as training data leads to rubber property estimation without the evaluation tests. In this paper, we propose a method that can generate electron microscope images from ingredient mix proportions. In the proposed method, we train a conditional style generative adversarial network utilizing pairs of ingredient mix proportions and electron microscope images. Experimental results showed that the effectiveness of the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) rubber materials / electron microscope image / generative adversarial network / image generation / ingredient mix proportions / / /  
Reference Info. IEICE Tech. Rep.
Paper #  
Date of Issue  
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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Conference Information
Committee IE ITS ITE-MMS ITE-ME ITE-AIT  
Conference Date 2021-02-18 - 2021-02-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image Processing, etc. 
Paper Information
Registration To ITE-ME 
Conference Code 2021-02-IE-ITS-MMS-ME-AIT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Note on Electron Microscope Image Generation from Mix Proportion via Conditional Style Generative Adversarial Network for Rubber Materials 
Sub Title (in English)  
Keyword(1) rubber materials  
Keyword(2) electron microscope image  
Keyword(3) generative adversarial network  
Keyword(4) image generation  
Keyword(5) ingredient mix proportions  
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1st Author's Name Rintaro Yanagi  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Ren Togo  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Takahiro Ogawa  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
4th Author's Name Miki Haseyama  
4th Author's Affiliation Hokkaido University (Hokkaido Univ.)
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Speaker
Date Time 2021-02-19 14:15:00 
Presentation Time 10 
Registration for ITE-ME 
Paper #  
Volume (vol) IEICE-120 
Number (no)  
Page  
#Pages IEICE- 
Date of Issue  


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