WEKO3
アイテム
Fast and High-performance Multi-convolution Deep Neural Network Structure with Residuals
http://hdl.handle.net/10131/00012499
http://hdl.handle.net/10131/00012499be2e7c8e-0041-4c73-9bd7-57a1b005bf7c
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
3_MECH18.pdf (664.0 kB)
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| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2019-05-23 | |||||
| タイトル | ||||||
| タイトル | Fast and High-performance Multi-convolution Deep Neural Network Structure with Residuals | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 主題 | Convolution, Training, Kernel, Neural networks, Benchmark testing, Computer architecture, Standards | |||||
| 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 | |||||
| 著者 |
Yunpeng, Wang
× Yunpeng, Wang× Yasutaka, Fujimoto |
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| 著者所属 | ||||||
| 値 | Department of Electrical and Computer Engineering, Yokohama National University | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Very deep convolution neural networks show a great improvement over competitive benchmarks. But the depth also brings extremely high computational cost. In this paper, inspired by Inception module, we introduce a new convolutional neural network module that combines residual structure and multiple convolution. A residual structure is mainly adopted to solve the gradient vanishing problem. And unlike the concatenation in the inception module, multiple convolution is used to find the most proper feature maps through self-optimizing training. With this module, we no longer need to carefully optimize the convolution structure of network, but can attain state-of-the-art results on CIFAR-10, MNIST and CIFAR-100 with 26 layers and only 19k parameters. | |||||
| 書誌情報 |
2018 12th France-Japan and 10th Europe-Asia Congress on Mechatronics 発行日 2018-10-18 |
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| DOI | ||||||
| 関連タイプ | isVersionOf | |||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.1109/MECATRONICS.2018.8495837 | |||||
| 権利 | ||||||
| 権利情報 | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||
| 著者版フラグ | ||||||
| 出版タイプ | AM | |||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||
| 出版者 | ||||||
| 出版者 | IEEE | |||||
