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Paper Abstract and Keywords
Presentation 2022-06-09 15:00
[Invited Talk] Advanced applications of machine learning techniques towards high-performance and cost-effective visual inspection AI
Terumasa Tokunaga (Kyutech) SIS2022-6
Abstract (in Japanese) (See Japanese page) 
(in English) Visual inspection is an essential step for quality control in manufacturing. Recently, many researchers have shown great interest in the establishment of visual inspection AI driven by breakthroughs in deep learning. Supervised approaches require the large number of defective and defect-free sample images for training classifiers. However, in many practical situations, the collection of defective images is quite costly. We are now developing novel anomaly detection techniques towards cost-effective and high-performance visual inspection AI. Our approaches rely on advanced application of machine learning techniques, including unsupervised learning, semi-supervised learning and visual attention mechanism. This presentation will report the current status and scope of our projects including recent collaborative researches with manufacturing companies.
Keyword (in Japanese) (See Japanese page) 
(in English) visual inspection AI / anomaly detection / deep neural network / generative adversarial network / visual attention mechanism / semi-supervised learning / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 62, SIS2022-6, pp. 30-30, June 2022.
Paper # SIS2022-6 
Date of Issue 2022-06-02 (SIS) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee SIS IPSJ-AVM  
Conference Date 2022-06-09 - 2022-06-10 
Place (in Japanese) (See Japanese page) 
Place (in English) KIT(Wakamatsu Campus) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Intelligent Multimedia Systems, Applied Embedded Systems, Three-Dimensional Image Technology (3DIT), etc. 
Paper Information
Registration To SIS 
Conference Code 2022-06-SIS-AVM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Advanced applications of machine learning techniques towards high-performance and cost-effective visual inspection AI 
Sub Title (in English)  
Keyword(1) visual inspection AI  
Keyword(2) anomaly detection  
Keyword(3) deep neural network  
Keyword(4) generative adversarial network  
Keyword(5) visual attention mechanism  
Keyword(6) semi-supervised learning  
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1st Author's Name Terumasa Tokunaga  
1st Author's Affiliation Kyushu Institute of Technology (Kyutech)
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Speaker Author-1 
Date Time 2022-06-09 15:00:00 
Presentation Time 60 minutes 
Registration for SIS 
Paper # SIS2022-6 
Volume (vol) vol.122 
Number (no) no.62 
Page p.30 
#Pages
Date of Issue 2022-06-02 (SIS) 


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