An Information-theoretic Visual Analysis Framework for Convolutional Neural Networks
This addresses the need for theoretical explanations of neural networks for researchers in visualization and machine learning, but it is incremental as it builds on existing information-theoretic approaches.
The paper tackles the problem of understanding how Convolutional Neural Networks (CNNs) work by combining information theory and visualization techniques, resulting in a system called CNNSlicer that demonstrates effectiveness on MNIST and CIFAR-10 datasets.
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.