关键词: 高斯函数, 池化, 加权平均, 卷积神经网络, CIFAR-10, Fer2013, 德国交通标志识别基准

Abstract:

Aiming at the problem that the traditional pooling algorithms in Convolutional Neural Network （CNN） cannot well consider the correlation between each element in the pooling domain and the features contained in the pooling domain， a pooling algorithm based on Gaussian function was proposed. Firstly， according to the value of each element in the pooling domain and the maximum value of all elements， the three parameter values of the Gaussian function were calculated. Then， the Gaussian function was used to calculate the weights of all elements in the pooling domain. Finally， the weighted average value of all elements in the pooling domain was calculated according to these weights. Finally， the obtained value was used as the pooling result. LeNet5， VGG （Visual Geometry Group）16， ResNet （Residual Network）18 and MobileNet v3 were selected as the experimental models. Experiments were carried out on public datasets CIFAR-10， Fer2013 and German Traffic Sign Recognition Benchmark （GTSRB）， and max pooling， average pooling， random pooling， mixed pooling， fuzzy pooling， fused random pooling and soft pooling were selected to compare. Experimental results show that the proposed algorithm improves the accuracy by 0.5 percentage points to 6 percentage points compared with other algorithms on the three datasets， and the running efficiency of the proposed algorithm is higher than those of the other pooling algorithms except max pooling algorithm and average pooling algorithm， so as to verify that the proposed algorithm is effective and suitable for the situations where the operation time demand is not high but the accuracy demand is high.

Key words: Gaussian function, pooling, weighted average, Convolutional Neural Network (CNN), CIFAR-10, Fer2013, German Traffic Sign Recognition Benchmark (GTSRB)