Paper Abstract and Keywords |
Presentation |
2021-12-17 10:45
Data augmentation for domain adaptation by class consistency using soft target Wataru Imaeda (Kyocera Corp.), Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.) PRMU2021-49 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Domain adaptation is a task to acquire sufficient discrimination performance between images with different domains in learning and evaluation. In this task, there are approaches such as acquisition of domain invariant feature quantity and elimination of domain gap using style transformation. In the approach by the style transformation, the input/output relation in the style transformation is 1: 1 in the conventional domain adaptation technique. Therefore, the transformed image with changed class information does not contribute to the improvement of the discrimination performance. In this paper, we propose a data augmentation method for domain adaptation using style transformation considering class information. In the proposed method, the transformation suitable for domain adaptation is realized by considering class consistency in image transformation using Soft target. In addition, by applying Generative Adversarial Networks, the style augmentation which generates multiple images with different styles from one image is carried out. We also show that the style augmentation is effective in identification using the Test-time Augmentation. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Style transfer / Domain adaptation / Test-time Augmentation / GANs / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 304, PRMU2021-49, pp. 136-141, Dec. 2021. |
Paper # |
PRMU2021-49 |
Date of Issue |
2021-12-09 (PRMU) |
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) |
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PRMU2021-49 |
Conference Information |
Committee |
PRMU |
Conference Date |
2021-12-16 - 2021-12-17 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
PRMU |
Conference Code |
2021-12-PRMU |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Data augmentation for domain adaptation by class consistency using soft target |
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Keyword(1) |
Style transfer |
Keyword(2) |
Domain adaptation |
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Test-time Augmentation |
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GANs |
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1st Author's Name |
Wataru Imaeda |
1st Author's Affiliation |
Kyocera Corporation (Kyocera Corp.) |
2nd Author's Name |
Tsubasa Hirakawa |
2nd Author's Affiliation |
Chubu University (Chubu Univ.) |
3rd Author's Name |
Takayoshi Yamashita |
3rd Author's Affiliation |
Chubu University (Chubu Univ.) |
4th Author's Name |
Hironobu Fujiyoshi |
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Chubu University (Chubu Univ.) |
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Speaker |
Author-1 |
Date Time |
2021-12-17 10:45:00 |
Presentation Time |
15 minutes |
Registration for |
PRMU |
Paper # |
PRMU2021-49 |
Volume (vol) |
vol.121 |
Number (no) |
no.304 |
Page |
pp.136-141 |
#Pages |
6 |
Date of Issue |
2021-12-09 (PRMU) |
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