- 【Updated on May 12, 2025】 Integration of CiNii Dissertations and CiNii Books into CiNii Research
- Trial version of CiNii Research Automatic Translation feature is available on CiNii Labs
- Suspension and deletion of data provided by Nikkei BP
- Regarding the recording of “Research Data” and “Evidence Data”
Experimental Investigation of Neural Network with Deep Residual Block
-
- MORISHITA Hiroki
- Osaka Prefecture University
-
- INOUE Katsufumi
- Osaka Prefecture University
-
- YOSHIOKA Michifumi
- Osaka Prefecture University
Bibliographic Information
- Other Title
-
- 深いResidual blockをもったニューラルネットワークの実験的検討
Search this article
Description
Recently, convolutional neural network has commonly used for classification task. Many conventional methods employ residual network architecture that repeatedly stack a module called residual block. In this paper, we propose a new module architecture to enhance representational power of modules. The module utilizes a method used in DenseNet to make architecture deeper. Each layer in the module is connected to every other layer in a feed-forward fashion. Our experiments conducted with CIFAR10 and CIFAR100 show that our method outperforms conventional methods in term of parameter efficiency for error rate.
Journal
-
- 電子情報通信学会論文誌D 情報・システム
-
電子情報通信学会論文誌D 情報・システム J101-D (8), 1140-1149, 2018-08-01
The Institute of Electronics, Information and Communication Engineers
- Tweet
Details 詳細情報について
-
- CRID
- 1390845712976097664
-
- ISSN
- 18810225
- 18804535
-
- Text Lang
- ja
-
- Data Source
-
- JaLC
-
- Abstract License Flag
- Disallowed