Denoising Convolutional Neural Network

Abstract

Convolutional Neural Network (CNN) is a kind of deep artificial neural network. CNN has kinds of merits, such as multidimensional data input, and fewer parameters. However, the network always has the problem of overfitting due to lots of connection in the full connection layer. In order to overcome the overfitting problem, the denoising method is used to corrupt input data and hidden unit output which will enforce the network learning a better feature representations of the sample data. In the simulation, some situations are considered, such as input data corruption and hidden unit output corruption, and a comparison is exhibited.