Research on a New Convolutional Neural Network Model Combined with Random Edges Adding

• URL: http://arxiv.org/abs/2003.07794v2
• Date: Fri, 28 Aug 2020 07:58:22 GMT
• Title: Research on a New Convolutional Neural Network Model Combined with Random Edges Adding
• Authors: Xuanyu Shu, Jin Zhang, Sen Tian, Sheng chen and Lingyu Chen
• Abstract summary: A random edge adding algorithm is proposed to improve the performance of convolutional neural network model. The simulation results show that the model recognition accuracy and training convergence speed are greatly improved by random edge adding reconstructed models.
• Score: 10.519799195357209
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is always a hot and difficult point to improve the accuracy of convolutional neural network model and speed up its convergence. Based on the idea of small world network, a random edge adding algorithm is proposed to improve the performance of convolutional neural network model. This algorithm takes the convolutional neural network model as a benchmark, and randomizes backwards and cross-layer connections with probability p to form a new convolutional neural network model. The proposed idea can optimize the cross layer connectivity by changing the topological structure of convolutional neural network, and provide a new idea for the improvement of the model. The simulation results based on Fashion-MINST and cifar10 data set show that the model recognition accuracy and training convergence speed are greatly improved by random edge adding reconstructed models with aprobability p = 0.1.

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