基于反卷积特征提取的深度卷积神经网络学习

Deep convolution neural network learning based on deconvolution feature extraction

在深度卷积神经网络的学习过程中,卷积核的初始值通常是随机赋值的.另外,基于梯度下降法的网络参数学习法通常会导致梯度弥散现象.鉴于此,提出一种基于反卷积特征提取的深度卷积神经网络学习方法.首先,采用无监督两层堆叠反卷积神经网络从原始图像中学习得到特征映射矩阵;然后,将该特征映射矩阵作为深度卷积神经网络的卷积核,对原始图像进行逐层卷积和池化操作;最后,采用附加动量系数的小批次随机梯度下降法对深度卷积网络微调以避免梯度弥散问题.在MNIST、CIFAR-10和CIFAR-100数据集上的实验结果表明,所提出方法可有效提高图像分类精度.

During the learning process of the deep convolution neural network（DCNN）, the initial values of convolution kernels are usually randomly assigned. In addition, the learning rule of network parameters based on gradient descent usually results in gradient vanishing phenomenon. Aiming at the above problems, a learning method for the DCNN based on deconvolution feature extraction is proposed. Firstly, an unsupervised two-layer stacked deconvolution neural network is used to learn feature mapping matrixes from the original images. Then, the learned feature mapping matrixes are used as the convolution kernels to convolve and pool with the images in a layer-wise manner. Finally, the DCNN is fine-tuned by using the mini-batch stochastic gradient descent method with a momentum coefficient, which can avoid the gradient vanishing problem. Experimental results on MNIST, CIFAR-10 and CIFAR-100 data sets show that, the proposed method can effectively improve the accuracy of image classification.