WEKO3
アイテム
Bidirectional 2D reservoir computing for image anomaly detection without any training
http://hdl.handle.net/10228/0002001294
http://hdl.handle.net/10228/0002001294b9139d87-4fb6-4d93-9d0c-72649ba0a5b3
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
LaSEINE-2023_063.pdf (17.8 MB)
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| アイテムタイプ | 共通アイテムタイプ(1) | |||||||||||
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| 公開日 | 2025-02-12 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Bidirectional 2D reservoir computing for image anomaly detection without any training | |||||||||||
| 言語 | en | |||||||||||
| その他のタイトル | ||||||||||||
| その他のタイトル | Bidirectional 2D Reservoir Computing for Image Anomaly Detection without any Training | |||||||||||
| 言語 | en | |||||||||||
| 著者 |
Nakanishi, Keiichi
× Nakanishi, Keiichi
× 徳永, 旭将
WEKO
25036
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| 著作権関連情報 | ||||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by-nc-nd/4.0/deed | |||||||||||
| 権利情報 | This work is licensed under a Creative Commons Attribution Non Commercial, No Derivatives 4.0 License. | |||||||||||
| 著作権関連情報 | ||||||||||||
| 権利情報 | Copyright (c) IEICE 2024 | |||||||||||
| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | Image anomaly detection is a crucial task in computer vision, where convolutional neural networks (CNN) often deliver exceptional performances. Hardware implementation of machine learning models is also important for achieving inference speed-up and power savings. However, the massive number of CNN parameters poses challenges for hardware implementation. This study introduces reservoir computing (RC) to create a compact image processor without training, thereby enabling scalable deployment. Our proposed bidirectional 2-dimensional reservoir computing (BiRC2D) is a feature extractor based on RC. Experiments conducted on the MVTec AD dataset, a benchmark dataset for real-world anomaly detection task, validated the efficacy of BiRC2D when integrated into the patch distribution modeling (PaDiM) framework. The mean intersection over union (mIoU) score from PaDiM with BiRC2D outperformed or was comparable to the mIoU score from PaDiM with ResNet-50 in several categories while reducing the parameter count by up to 98%. | |||||||||||
| 言語 | en | |||||||||||
| 書誌情報 |
en : Nonlinear Theory and Its Applications, IEICE 巻 15, 号 4, p. 838-850, 発行日 2024-10-01 |
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| 出版社 | ||||||||||||
| 出版者 | 電子情報通信学会 | |||||||||||
| 言語 | ja | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | reservoir computing | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | echo state network | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | deep learning | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | computer vision | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | image anomaly detection | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||
| 資源タイプ | journal article | |||||||||||
| 出版タイプ | ||||||||||||
| 出版タイプ | VoR | |||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
| DOI | ||||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | https://doi.org/10.1587/nolta.15.838 | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||
| 収録物識別子 | 2185-4106 | |||||||||||
| 査読の有無 | ||||||||||||
| 値 | yes | |||||||||||
| 研究者情報 | ||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/100000804_ja.html | |||||||||||
| 論文ID（連携） | ||||||||||||
| 値 | 10441746 | |||||||||||
| 連携ID | ||||||||||||
| 値 | 12438 | |||||||||||
