Convolutional Neural Network with Corrupted Input

Abstract

Convolutional neural network is a model of deep neural network, which uses the convolution and sub sampling to realize feature extraction. However, the network is easy to over fitting. In this paper, the denoising method is used to corrupt the sample and force the network to learn the better representations to overcome the over fitting problem. The generalization of the convolutional neural network will be enhanced by this. The simulations exhibit the learning process.