Conceptual Content in Deep Convolutional Neural Networks: An analysis into multi-faceted properties of neurons

Abstract

In this paper, convolutional layers of pre-trained VGG16 model are analyzed. The analysis is based on the responses of neurons to the images of classes in ImageNet database. First, a visualization method is proposed in order to illustrate the learned content of each neuron. Next, single and multi-faceted neurons are investigated based on the diversity of neurons responses to different category of objects. Finally, neuronal similarities at each layer are computed and compared. The results demonstrate that the neurons in lower layers exhibit a multi-faceted behavior, whereas the majority of neurons in higher layers com-prise single-faceted property and tend to respond to a smaller number of concepts.