参数池化卷积神经网络图像分类方法

传统的卷积神经网络使用池化层对信息进行降维操作，通常会造成信息损失，从而影响网络的表达能力.针对这一问题，使用参数池化层（Parameterized Pooling Layer）替代传统卷积神经网络中的池化层，提出参数池化卷积神经网络（Parameterized Pooling CNN，PPCNN）.参数池化层在仅仅增加了少量网络参数的情况下，最大可能的保留了卷积神经网络中希望被保留下来的特征；同时，由于增加了池化层前向传播的信息，从而影响了反向传播算法中权值的更新，网络收敛速度更快；实验结果表明，PPCNN模型与传统卷积神经网络模型以及部分改进模型相比，参数池化卷积神经网络模型是有效的.

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.