Convolutional modulation theory: A bridge between convolutional neural networks and signal modulation theory

Abstract

Although there have been a lot of researches on convolutional neural networks (CNNs), still what happens in this black box remains a mystery. In this paper, we establish the connection between CNNs and signal modulation. From a signal modulation point of view, the forward-propagation process of CNNs can be explained as a process of modulating the input signals to the vicinity of a special energy spectrum distribution, and the back-propagation process is searching for the appropriate distribution which is better for classification or other tasks. Several experiments have been carried out to verify the modulated explanation of CNNs. Furthermore, we verify that modulating the signal to the appropriate energy spectrum distribution in advance can effectively improve the classification and segmentation accuracy.