TY - GEN
T1 - Auto-DenseNet
T2 - 5th International Conference on Intelligent Autonomous Systems, ICoIAS 2022
AU - Zhai, Leilei
AU - Wang, Dianwei
AU - Fang, Jie
AU - Xu, Zhijie
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - To address the issue of time-consuming and laborious design of network structure parameters and connection methods, in this paper we propose an evolutionary algorithm-based DenseNet network optimization method (Auto Dense Convolutional Network, Auto-DenseNet). Firstly, the internal parameters of DenseNet are encoded to generate an initial population, and crossover and variation operators are designed to ensure the effectiveness of the evolution of offspring. Secondly, the dataset is imported into the decoded individual evaluation fitness. And then, the superior individuals are selected to enter the next iteration. Finally, the optimized network individuals are selected for the image classification task. After tuning validation on the MNIST-RD dataset, tests were conducted on three widely used benchmark image classification datasets. The experimental results demonstrate that the network structure searched by the Auto-DenseNet algorithm outperforms most existing structures in terms of classification performance, and the number of network parameters.
AB - To address the issue of time-consuming and laborious design of network structure parameters and connection methods, in this paper we propose an evolutionary algorithm-based DenseNet network optimization method (Auto Dense Convolutional Network, Auto-DenseNet). Firstly, the internal parameters of DenseNet are encoded to generate an initial population, and crossover and variation operators are designed to ensure the effectiveness of the evolution of offspring. Secondly, the dataset is imported into the decoded individual evaluation fitness. And then, the superior individuals are selected to enter the next iteration. Finally, the optimized network individuals are selected for the image classification task. After tuning validation on the MNIST-RD dataset, tests were conducted on three widely used benchmark image classification datasets. The experimental results demonstrate that the network structure searched by the Auto-DenseNet algorithm outperforms most existing structures in terms of classification performance, and the number of network parameters.
KW - dense connection network
KW - evolutionary algorithm
KW - image classification
KW - Neural network structure optimization
UR - http://www.scopus.com/inward/record.url?scp=85142446215&partnerID=8YFLogxK
U2 - 10.1109/ICoIAS56028.2022.9931240
DO - 10.1109/ICoIAS56028.2022.9931240
M3 - Conference Proceeding
AN - SCOPUS:85142446215
T3 - 5th International Conference on Intelligent Autonomous Systems, ICoIAS 2022
SP - 387
EP - 393
BT - 5th International Conference on Intelligent Autonomous Systems, ICoIAS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 September 2022 through 25 September 2022
ER -