Abstract—Deep convolutional neural networks (DCNNs) have achieved state-of-the-art results for image recognition. However, these DCNNs with complex structure consist of many layers like convolutional layers, which require high time and computational complexity for training. Therefore, we propose a novel shallow convolutional neural network (SCNND) with dropout to address the problems of the DCNNs for image recognition. The SCNND with 4 layers can fast learning the features of the images, using dropout technology between two convolutional layers to improve recognition performance. Compared to the SCNNs, our SCNND includes 4 layers, with low time complexity and parameters. Experimental results show that our SCNND outperforms shallow CNN methods on Fashion-MNIST dataset.

Index Terms—Deep convolutional neural networks, shallow convolutional neural network, dropout, image recognition.

Fangyuan Lei, Xun Liu, Jianjian Jiang, Qingyun Dai, Hongyu Liu, and Mengying Hu are School of Electronic and Information, Guangdong Polytechnic Normal University, Guangzhou, China.

Cite:Fangyuan Lei, Xun Liu, Jianjian Jiang, Qingyun Dai, Hongyu Liu, Mengying Hu, "Shallow Convolutional Neural Network for Image Recognition," International Journal of Computer and Electrical Engineering vol. 11, no. 4, pp. 192-197, 2019.