Simplified residual structure and fast deep residual networks
文献类型：期刊论文
| 作者 | H.-J. Yang; E.-S. Wang; Y.-X. Sui; F. Yan and Y. Zhou |
| 刊名 | Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)
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| 出版日期 | 2022 |
| 卷号 | 52期号:6页码:1413-1421 |
| ISSN号 | 16715497 |
| DOI | 10.13229/j.cnki.jdxbgxb20210027 |
| 英文摘要 | In order to address the problem of slow training of the current deep ResNets model, a novel residual structure is designed. Compared with the typical residual structure, the structure only contains a Batch Normalization and ReLU, which reduces training time and improves the training speed by reducing the amount of calculation in the network training process. The comparative experiments are carried out on the CIFAR10/100 image classification database. The classification error rate of 110 layers networks constructed by this method on CIFAR10 and CIFAR100 is 5.29% and 24.80%, respectively. The classification error rate of 110-ResNet is 5.75% and 26.02%, respectively. Training the network takes 133.47 (this method) and 208.26 (ResNet) seconds per epoch, increased by 35.91%. The results show that the network structure greatly improves the training speed while ensuring the classification performance, and has better practical value. 2022, Jilin University Press. All right reserved. |
| URL标识 | 查看原文 |
| 源URL | [http://ir.ciomp.ac.cn/handle/181722/67079]  |
| 专题 | 中国科学院长春光学精密机械与物理研究所 |
| 推荐引用方式 GB/T 7714 | H.-J. Yang,E.-S. Wang,Y.-X. Sui,et al. Simplified residual structure and fast deep residual networks[J]. Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition),2022,52(6):1413-1421. |
| APA | H.-J. Yang,E.-S. Wang,Y.-X. Sui,&F. Yan and Y. Zhou.(2022).Simplified residual structure and fast deep residual networks.Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition),52(6),1413-1421. |
| MLA | H.-J. Yang,et al."Simplified residual structure and fast deep residual networks".Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) 52.6(2022):1413-1421. |
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