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| Presentation | 2022-12-05 16:00 Accuracy Improvement of Real Image Classification by Style Transfer Using Training Data Created from 3DCG Takeru Inoue, Youichi Tomita (Meiji Univ.), Kouji Gakuta, Etsuji Yamada, Aoi Kariya (Digital Printing & Solutions), Masakazu Kinosada, Yujiro Kitaide (Shinsei Printing), Ryusuke Miyamoto (Meiji Univ.)  SIS2022-30 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In general, in order to achieve sufficient accuracy by machine learning in practical applications, training data appropriate for the target that requires huge cost for creation is indispensable. A novel framework was proposed to reduce the cost required for dataset creation by using three-dimensional models of target object to generate two-dimensional images with labels for classification. However, existing work shows that classification accuracy of actual images becomes worse when a classifier is trained using rendered images from three-dimensional models. This paper proposes a training scheme that learns shapes of target objects more than the standard way to improve classification accuracy when a domain gap exists. Experimental results showed that the classification accuracy was improved by 11% when style transfer was applied to training data generating from three-dimensional models. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | deep learning / computer graphics / style transfer / domain gap / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 293, SIS2022-30, pp. 38-43, Dec. 2022. | |
| Paper # | SIS2022-30 | |
| Date of Issue | 2022-11-28 (SIS) | |
| 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 | SIS2022-30 |
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| Conference Information | |
| Committee | SIS |
| Conference Date | 2022-12-05 - 2022-12-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | SIS |
| Conference Code | 2022-12-SIS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Accuracy Improvement of Real Image Classification by Style Transfer Using Training Data Created from 3DCG |
| Sub Title (in English) | |
| Keyword(1) | deep learning |
| Keyword(2) | computer graphics |
| Keyword(3) | style transfer |
| Keyword(4) | domain gap |
| Keyword(5) | |
| Keyword(6) | |
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| Keyword(8) | |
| 1st Author's Name | Takeru Inoue |
| 1st Author's Affiliation | Meiji University (Meiji Univ.) |
| 2nd Author's Name | Youichi Tomita |
| 2nd Author's Affiliation | Meiji University (Meiji Univ.) |
| 3rd Author's Name | Kouji Gakuta |
| 3rd Author's Affiliation | Digital Printing & Solutions Co., Ltd. (Digital Printing & Solutions) |
| 4th Author's Name | Etsuji Yamada |
| 4th Author's Affiliation | Digital Printing & Solutions Co., Ltd. (Digital Printing & Solutions) |
| 5th Author's Name | Aoi Kariya |
| 5th Author's Affiliation | Digital Printing & Solutions Co., Ltd. (Digital Printing & Solutions) |
| 6th Author's Name | Masakazu Kinosada |
| 6th Author's Affiliation | Shinsei Printing Co., Ltd. (Shinsei Printing) |
| 7th Author's Name | Yujiro Kitaide |
| 7th Author's Affiliation | Shinsei Printing Co., Ltd. (Shinsei Printing) |
| 8th Author's Name | Ryusuke Miyamoto |
| 8th Author's Affiliation | Meiji University (Meiji Univ.) |
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| Speaker | Author-1 |
| Date Time | 2022-12-05 16:00:00 |
| Presentation Time | 20 minutes |
| Registration for | SIS |
| Paper # | SIS2022-30 |
| Volume (vol) | vol.122 |
| Number (no) | no.293 |
| Page | pp.38-43 |
| #Pages | 6 |
| Date of Issue | 2022-11-28 (SIS) |
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