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| Presentation | 2018-09-20 09:40 Image Patchwork Data Augmentation Ryo Takahashi, Takashi Matsubara, Kuniaki Uehara (Kobe Univ.)  PRMU2018-43 IBISML2018-20 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Deep convolutional neural networks (CNNs) have demonstrated remarkable results thanks to their numerous parameters. However, such numerous parameters against the variety of training samples often have a risk of overfitting. To solve this problem, data augmentation methods have been proposed so far. Data augmentation increases the variety of data by flipping, cropping, resizing and re-colorizing, and it leads deep CNNs to achieve higher performance by preventing overfitting. In this study, we propose a new data augmentation technique called random image cropping and patching (RICAP), which randomly crops four images and patches them to create a new training image. We evaluate RICAP with current state-of-the-art CNNs (e.g., shake-shake regularization model) by comparison with competitive data augmentation techniques such as cutout and mixup; RICAP achieves a new state-of-the-art test error of 2.23% on CIFAR-10. We also confirm that deep CNNs with RICAP achieve better results on CIFAR-100 and ImageNet than those with other techniques. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Data Augmentation / Image Classification / Convolutional Neural Network / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 220, IBISML2018-20, pp. 47-54, Sept. 2018. | |
| Paper # | IBISML2018-20 | |
| Date of Issue | 2018-09-13 (PRMU, IBISML) | |
| 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 | PRMU2018-43 IBISML2018-20 |
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| Conference Information | |
| Committee | PRMU IBISML IPSJ-CVIM |
| Conference Date | 2018-09-20 - 2018-09-21 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2018-09-PRMU-IBISML-CVIM |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Image Patchwork Data Augmentation |
| Sub Title (in English) | |
| Keyword(1) | Data Augmentation |
| Keyword(2) | Image Classification |
| Keyword(3) | Convolutional Neural Network |
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| 1st Author's Name | Ryo Takahashi |
| 1st Author's Affiliation | Kobe University (Kobe Univ.) |
| 2nd Author's Name | Takashi Matsubara |
| 2nd Author's Affiliation | Kobe University (Kobe Univ.) |
| 3rd Author's Name | Kuniaki Uehara |
| 3rd Author's Affiliation | Kobe University (Kobe Univ.) |
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| Speaker | Author-1 |
| Date Time | 2018-09-20 09:40:00 |
| Presentation Time | 10 minutes |
| Registration for | IBISML |
| Paper # | PRMU2018-43, IBISML2018-20 |
| Volume (vol) | vol.118 |
| Number (no) | no.219(PRMU), no.220(IBISML) |
| Page | pp.47-54 |
| #Pages | 8 |
| Date of Issue | 2018-09-13 (PRMU, IBISML) |
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