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| Presentation | 2021-12-16 11:00 Anomaly Detection using PatchCore with Self-attention module Yuki Takena (Shizuoka Univ.), Yoshiki Nota, Rinpei Mochizuki (Meidensya Corp.), Itaru Matsumura (Railway Technical Research Inst.), Gosuke Ohashi (Shizuoka Univ.)  PRMU2021-29 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In recent years, in visual inspection of industrial products using deep learning, There are many models that achieve excellent accuracy in detecting anomalies in local area such as scratches and stains. However, there is a problem that it is weak in detecting anomalies in cooccurrence relations between parts. Therefore, we focus on Transformer’s Self-attention module, which can determine the relationship between pixels, and enable anomaly detection of cooccurrence relationships. By introducing a Self-attention module into PatchCore, which is a State-of-the-art of MVTec AD Datasets of the anomaly detection benchmark, we propose a model that can identify anomalies in the cooccurrence relationships between parts and localize the parts with different relationships. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Deep learning / Anomaly detection / Unsupervised learning / PatchCore / Self-attention / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 304, PRMU2021-29, pp. 31-36, Dec. 2021. | |
| Paper # | PRMU2021-29 | |
| Date of Issue | 2021-12-09 (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 | PRMU2021-29 |
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| Conference Information | |
| Committee | PRMU |
| Conference Date | 2021-12-16 - 2021-12-17 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2021-12-PRMU |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Anomaly Detection using PatchCore with Self-attention module |
| Sub Title (in English) | |
| Keyword(1) | Deep learning |
| Keyword(2) | Anomaly detection |
| Keyword(3) | Unsupervised learning |
| Keyword(4) | PatchCore |
| Keyword(5) | Self-attention |
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| 1st Author's Name | Yuki Takena |
| 1st Author's Affiliation | Shizuoka University (Shizuoka Univ.) |
| 2nd Author's Name | Yoshiki Nota |
| 2nd Author's Affiliation | Meidensya Corporation (Meidensya Corp.) |
| 3rd Author's Name | Rinpei Mochizuki |
| 3rd Author's Affiliation | Meidensya Corporation (Meidensya Corp.) |
| 4th Author's Name | Itaru Matsumura |
| 4th Author's Affiliation | Railway Technical Research Institute (Railway Technical Research Inst.) |
| 5th Author's Name | Gosuke Ohashi |
| 5th Author's Affiliation | Shizuoka University (Shizuoka Univ.) |
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| Speaker | Author-1 |
| Date Time | 2021-12-16 11:00:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | PRMU2021-29 |
| Volume (vol) | vol.121 |
| Number (no) | no.304 |
| Page | pp.31-36 |
| #Pages | 6 |
| Date of Issue | 2021-12-09 (PRMU) |
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