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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 |
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| 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 | 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 | SIS2022-6 |
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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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| Keyword(8) | |
| 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 | 1 |
| Date of Issue | 2022-06-02 (SIS) |
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