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| Presentation | 2019-12-12 14:55 Machine learning algorithms with quantized images and their influence Takayuki Osakabe, Yuma Kinoshita, Hitoshi Kiya (Tokyo Metro.Univ.)  SIS2019-27 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Recently, appling quantized images to machine learning algorithms is expected to enhance robustness against adversarial examples. However, quantizing data affects the performance of machine learning algorithms. In this paper, three quantized methods: linear quantization, lloyd-max quantization and error diffusion are applied to images respectively， and we consider the influence of the quantization in some machine learning algorithms including deep learning for image classification. Experimental results show that we can get high classification accuracy even when low bits (1 or 2bit) images quantized by lloyd-max quantization are used in SVM, KNN and Logistic Regression. The results also demonstrate that we can obtain almost the same classification accuracy as that of baseline if we carefully choose a quantized method and the number of bits under the use of each model. In deep learning with ResNet-20, the model gives high classification accuracy if both of training and test images are quantized by using an error diffusion algorithm with the same number of bits. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | linear-quantization / lloyd-max quantization / error diffusion / machine learning / deep learning / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 335, SIS2019-27, pp. 23-28, Dec. 2019. | |
| Paper # | SIS2019-27 | |
| Date of Issue | 2019-12-05 (SIS) | |
| ISSN | Print edition: ISSN 0913-5685 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 | SIS2019-27 |
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| Conference Information | |
| Committee | SIS |
| Conference Date | 2019-12-12 - 2019-12-13 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Okayama University of Science |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Smart Personal Systems, etc. |
| Paper Information | |
| Registration To | SIS |
| Conference Code | 2019-12-SIS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Machine learning algorithms with quantized images and their influence |
| Sub Title (in English) | |
| Keyword(1) | linear-quantization |
| Keyword(2) | lloyd-max quantization |
| Keyword(3) | error diffusion |
| Keyword(4) | machine learning |
| Keyword(5) | deep learning |
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| Keyword(8) | |
| 1st Author's Name | Takayuki Osakabe |
| 1st Author's Affiliation | Tokyo Metropolitan University (Tokyo Metro.Univ.) |
| 2nd Author's Name | Yuma Kinoshita |
| 2nd Author's Affiliation | Tokyo Metropolitan University (Tokyo Metro.Univ.) |
| 3rd Author's Name | Hitoshi Kiya |
| 3rd Author's Affiliation | Tokyo Metropolitan University (Tokyo Metro.Univ.) |
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| Speaker | Author-1 |
| Date Time | 2019-12-12 14:55:00 |
| Presentation Time | 20 minutes |
| Registration for | SIS |
| Paper # | SIS2019-27 |
| Volume (vol) | vol.119 |
| Number (no) | no.335 |
| Page | pp.23-28 |
| #Pages | 6 |
| Date of Issue | 2019-12-05 (SIS) |
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