Visualizing and Comparing AlexNet and VGG using Deconvolutional Layers
Convolutional Neural Networks (CNNs) have been keeping improving the performance on ImageNet classification since it is firstly successfully applied in the task in 2012. To achieve better performance, the complexity of CNNs is continually increasing with deeper and bigger architectures. Though CNNs achieved promising external classification behavior, understanding of their internal work mechanism is still limited. In this work, we attempt to understand the internal work mechanism of CNNs by probing the internal representations in two comprehensive aspects, i.e. visualizing patches in the representation spaces constructed by different layers, and visualizing visual information kept in each layer. We further compare CNNs with different depths and show the advantages brought by deeper architecture.