  Convolutional neural networks utilize a hierarchy of neural network layers.
The statistical aspects of information concentration in successive layers can
bring an insight into the feature abstraction process. We analyze the saliency
maps of these layers from the perspective of semiotics, also known as the study
of signs and sign-using behavior. In computational semiotics, this aggregation
operation (known as superization) is accompanied by a decrease of spatial
entropy: signs are aggregated into supersign. Using spatial entropy, we compute
the information content of the saliency maps and study the superization
processes which take place between successive layers of the network. In our
experiments, we visualize the superization process and show how the obtained
knowledge can be used to explain the neural decision model. In addition, we
attempt to optimize the architecture of the neural model employing a semiotic
greedy technique. To the extent of our knowledge, this is the first application
of computational semiotics in the analysis and interpretation of deep neural
networks.
