Parameterized Pooling Convolution Neural Network for Image Classification

Traditional convolutional neural network uses pooling layer to reduce the dimension of feature, which usually results in information loss, thus affecting the expression ability of the network. To solve this problem, the parameterized pooling layer is used to replace the pooling layer in the conventional convolutional neural network, and the parameterized pooling CNN (PPCNN) is proposed. In the case that only a few network parameters are added in the parameter pooling layer, it is possible to retain the desired features. At the same time, the forward propagation information of the pooling layer is added, which affects the update of weight in the backpropagation algorithm, and the network convergence speed is faster. Compared with the conventional convolutional neural network model and some improved models, experimental results show that the PPCNN model is effective. © 2020, Chinese Institute of Electronics. All right reserved.