Shallow Convolutional Neural Network for Image Recognition

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.