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Paper Abstract and Keywords
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
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
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.
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
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

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  
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 
Date of Issue 2019-12-05 (SIS) 

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