Information: Join today and make your research activities more affordable! Technical workshop participation fees and annual registration fees are available at member rates.
Notice: [Important] Announcement of Changes to Registration Fee Payment and Manuscript Upload Procedures for IEICE Technical Meetings
IEICE Technical Committee Submission System
Conference Paper's Information		 Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2022-03-10 17:30
Adversarial Training: A Survey
Hiroki Adachi, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.) PRMU2021-73
Abstract (in Japanese) (See Japanese page) 
(in English) Adversarial training (AT) is a training method that aims to obtain a robust model for defencing the adversarial attack by using adversarial examples (AEs).
Although AT improves the robustness of the model to AEs, it significantly decreases the classification accuracy to natural samples.
To overcome this problem, researchers proposed methods that approached from several perspectives.
In this paper, we survey AT and systematically summarize about research trends of AT.
Furthermore, we evaluate and compare the classification accuracy with the exact experimental details for the typical methods.
Moreover, we visualize the low dimensional feature space of the model applied to each method and evaluate the feature representation using some quantitative evaluation indices.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep learning / Adversarial examples / Adversarial training / Survey / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 427, PRMU2021-73, pp. 78-90, March 2022.
Paper # PRMU2021-73 
Date of Issue 2022-03-03 (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 PRMU2021-73

Conference Information
Committee PRMU IPSJ-CVIM  
Conference Date 2022-03-10 - 2022-03-11 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Differentiable rendering 
Paper Information
Registration To PRMU 
Conference Code 2022-03-PRMU-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Adversarial Training: A Survey 
Sub Title (in English)  
Keyword(1) Deep learning  
Keyword(2) Adversarial examples  
Keyword(3) Adversarial training  
Keyword(4) Survey  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Hiroki Adachi  
1st Author's Affiliation Chubu University (Chubu Univ.)
2nd Author's Name Tsubasa Hirakawa  
2nd Author's Affiliation Chubu University (Chubu Univ.)
3rd Author's Name Takayoshi Yamashita  
3rd Author's Affiliation Chubu University (Chubu Univ.)
4th Author's Name Hironobu Fujiyoshi  
4th Author's Affiliation Chubu University (Chubu Univ.)
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
21st Author's Name  
21st Author's Affiliation ()
22nd Author's Name  
22nd Author's Affiliation ()
23rd Author's Name  
23rd Author's Affiliation ()
24th Author's Name  
24th Author's Affiliation ()
25th Author's Name  
25th Author's Affiliation ()
26th Author's Name / /
26th Author's Affiliation ()
()
27th Author's Name / /
27th Author's Affiliation ()
()
28th Author's Name / /
28th Author's Affiliation ()
()
29th Author's Name / /
29th Author's Affiliation ()
()
30th Author's Name / /
30th Author's Affiliation ()
()
31st Author's Name / /
31st Author's Affiliation ()
()
32nd Author's Name / /
32nd Author's Affiliation ()
()
33rd Author's Name / /
33rd Author's Affiliation ()
()
34th Author's Name / /
34th Author's Affiliation ()
()
35th Author's Name / /
35th Author's Affiliation ()
()
36th Author's Name / /
36th Author's Affiliation ()
()
Speaker Author-1 
Date Time 2022-03-10 17:30:00 
Presentation Time 30 minutes 
Registration for PRMU 
Paper # PRMU2021-73 
Volume (vol) vol.121 
Number (no) no.427 
Page pp.78-90 
#Pages 13 
Date of Issue 2022-03-03 (PRMU) 

[Return to Top Page]

[Return to IEICE Web Page]
The Institute of Electronics, Information and Communication Engineers (IEICE), Japan