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| Presentation | 2020-10-09 15:40 [Short Paper] Regularized pooling Takato Otsuzuki, Hideaki Hayashi, Zheng Yuchen, Seiichi Uchida (Kyushu Univ)  PRMU2020-31 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In convolutional neural networks (CNNs), pooling operations play important roles such as dimensionality reduction and deformation compensation. In general, max pooling, which is the most widely used operation for local pooling, is performed independently for each kernel. However, the deformation may be spatially smooth over the neighboring kernels. This means that max pooling is too flexible to compensate for actual deformations. In other words, its excessive flexibility risks canceling the essential spatial differences between classes. In this paper, we propose textit{regularized pooling}, which enables the value selection direction in the pooling operation to be spatially smooth across adjacent kernels so as to compensate only for actual deformations. The results of experiments on handwritten character images and texture images showed that regularized pooling not only improves recognition accuracy but also accelerates the convergence of learning compared with conventional pooling operations. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Pooling operation / Convolutional neural networks / Deformation compensation / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 187, PRMU2020-31, pp. 84-89, Oct. 2020. | |
| Paper # | PRMU2020-31 | |
| Date of Issue | 2020-10-02 (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) | |
| Download PDF | PRMU2020-31 |
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| Conference Information | |
| Committee | PRMU |
| Conference Date | 2020-10-09 - 2020-10-10 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Recognition and understating of human |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2020-10-PRMU |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Regularized pooling |
| Sub Title (in English) | |
| Keyword(1) | Pooling operation |
| Keyword(2) | Convolutional neural networks |
| Keyword(3) | Deformation compensation |
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| 1st Author's Name | Takato Otsuzuki |
| 1st Author's Affiliation | Kyushu University (Kyushu Univ) |
| 2nd Author's Name | Hideaki Hayashi |
| 2nd Author's Affiliation | Kyushu University (Kyushu Univ) |
| 3rd Author's Name | Zheng Yuchen |
| 3rd Author's Affiliation | Kyushu University (Kyushu Univ) |
| 4th Author's Name | Seiichi Uchida |
| 4th Author's Affiliation | Kyushu University (Kyushu Univ) |
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| Speaker | Author-1 |
| Date Time | 2020-10-09 15:40:00 |
| Presentation Time | 10 minutes |
| Registration for | PRMU |
| Paper # | PRMU2020-31 |
| Volume (vol) | vol.120 |
| Number (no) | no.187 |
| Page | pp.84-89 |
| #Pages | 6 |
| Date of Issue | 2020-10-02 (PRMU) |
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