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| Presentation | 2021-12-17 15:15 Task-independent redundancy reduction method using regularization for efficient neural network training Charvi Vitthal, Florian Beye, Koichi Nihei, Hayato Itsumi (NEC)  PRMU2021-58 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Neural networks (NNs) are widely used for various applications in recent years. However, it is difficult for the NN to learn optimum amount of information due to under-fitting and over-fitting. One reason is the presence of repeated information or inoperative components, in other words, redundancies. Hence, mitigating redundancies is essential for improving accuracy. Current methods do not capture all the ways to reduce redundancies without changing the network architecture. This paper proposes a neural network training method to reduce the redundancies. We propose novel metrics to quantify redundancies and ways to compute them. We evaluate our method on different tasks: 2D object detection, 3D object detection and image classification. Experimental results show upto 4% increase in accuracy for 2D object detection task. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Redundancy / Regularization / Information / Neural Network / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 304, PRMU2021-58, pp. 188-194, Dec. 2021. | |
| Paper # | PRMU2021-58 | |
| 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) | |
| Download PDF | PRMU2021-58 |
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| 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) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2021-12-PRMU |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Task-independent redundancy reduction method using regularization for efficient neural network training |
| Sub Title (in English) | |
| Keyword(1) | Redundancy |
| Keyword(2) | Regularization |
| Keyword(3) | Information |
| Keyword(4) | Neural Network |
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| 1st Author's Name | Charvi Vitthal |
| 1st Author's Affiliation | NEC Corporation (NEC) |
| 2nd Author's Name | Florian Beye |
| 2nd Author's Affiliation | NEC Corporation (NEC) |
| 3rd Author's Name | Koichi Nihei |
| 3rd Author's Affiliation | NEC Corporation (NEC) |
| 4th Author's Name | Hayato Itsumi |
| 4th Author's Affiliation | NEC Corporation (NEC) |
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| Speaker | Author-1 |
| Date Time | 2021-12-17 15:15:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | PRMU2021-58 |
| Volume (vol) | vol.121 |
| Number (no) | no.304 |
| Page | pp.188-194 |
| #Pages | 7 |
| Date of Issue | 2021-12-09 (PRMU) |
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