  Despite the great success of Convolutional Neural Networks (CNNs) in Computer
Vision and Natural Language Processing, the working mechanism behind CNNs is
still under extensive discussions and research. Driven by a strong demand for
the theoretical explanation of neural networks, some researchers utilize
information theory to provide insight into the black box model. However, to the
best of our knowledge, employing information theory to quantitatively analyze
and qualitatively visualize neural networks has not been extensively studied in
the visualization community. In this paper, we combine information entropies
and visualization techniques to shed light on how CNN works. Specifically, we
first introduce a data model to organize the data that can be extracted from
CNN models. Then we propose two ways to calculate entropy under different
circumstances. To provide a fundamental understanding of the basic building
blocks of CNNs (e.g., convolutional layers, pooling layers, normalization
layers) from an information-theoretic perspective, we develop a visual analysis
system, CNNSlicer. CNNSlicer allows users to interactively explore the amount
of information changes inside the model. With case studies on the widely used
benchmark datasets (MNIST and CIFAR-10), we demonstrate the effectiveness of
our system in opening the blackbox of CNNs.
