  Convolutional neural network (CNN) models have seen advanced improvements in
performance in various domains, but lack of interpretability is a major barrier
to assurance and regulation during operation for acceptance and deployment of
AI-assisted applications. There have been many works on input interpretability
focusing on analyzing the input-output relations, but the internal logic of
models has not been clarified in the current mainstream interpretability
methods. In this study, we propose a novel hybrid CNN-interpreter through: (1)
An original forward propagation mechanism to examine the layer-specific
prediction results for local interpretability. (2) A new global
interpretability that indicates the feature correlation and filter importance
effects. By combining the local and global interpretabilities, hybrid
CNN-interpreter enables us to have a solid understanding and monitoring of
model context during the whole learning process with detailed and consistent
representations. Finally, the proposed interpretabilities have been
demonstrated to adapt to various CNN-based model structures.
