| Paper Abstract and Keywords |
| Presentation |
2019-03-06 15:00
Efficient Learning for Distillation of DNN by Self Distillation Jumpei Takagi, Motonobu Hattori (Univ of Yamanashi) NC2018-83 |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
Knowledge distillation is a method to create a superior student by using knowledge obtained from a trained teacher neural network. Recent studies have shown that much superior students can be obtained by distilling the trained student further as a teacher. Distilling the knowledge through multiple generations, however, takes a long time for learning. In this paper, we propose a self distillation method which can reduce both the number of generations and learning time for knowledge distillation. In self distillation, the most accurate network is obtained during intra-generation learning, and it is used as a teacher of intra-generational distillation. Our experiments for image classification task demonstrate that the proposed self distillation acquires high accuracy with fewer generations and less learning time than the conventional method. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Knowledge Distillation / Self Distillation / Deep Learning / Image Classification / / / / |
| Reference Info. |
IEICE Tech. Rep., vol. 118, no. 470, NC2018-83, pp. 209-214, March 2019. |
| Paper # |
NC2018-83 |
| Date of Issue |
2019-02-25 (NC) |
| 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 |
NC2018-83 |
| Conference Information |
| Committee |
NC MBE |
| Conference Date |
2019-03-04 - 2019-03-06 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
University of Electro Communications |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
|
| Paper Information |
| Registration To |
NC |
| Conference Code |
2019-03-NC-MBE |
| Language |
Japanese |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
Efficient Learning for Distillation of DNN by Self Distillation |
| Sub Title (in English) |
|
| Keyword(1) |
Knowledge Distillation |
| Keyword(2) |
Self Distillation |
| Keyword(3) |
Deep Learning |
| Keyword(4) |
Image Classification |
| Keyword(5) |
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| 1st Author's Name |
Jumpei Takagi |
| 1st Author's Affiliation |
University of Yamanashi (Univ of Yamanashi) |
| 2nd Author's Name |
Motonobu Hattori |
| 2nd Author's Affiliation |
University of Yamanashi (Univ of Yamanashi) |
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| Speaker |
Author-1 |
| Date Time |
2019-03-06 15:00:00 |
| Presentation Time |
25 minutes |
| Registration for |
NC |
| Paper # |
NC2018-83 |
| Volume (vol) |
vol.118 |
| Number (no) |
no.470 |
| Page |
pp.209-214 |
| #Pages |
6 |
| Date of Issue |
2019-02-25 (NC) |