  The reasonable definition of semantic interpretability presents the core
challenge in explainable AI. This paper proposes a method to modify a
traditional convolutional neural network (CNN) into an interpretable
compositional CNN, in order to learn filters that encode meaningful visual
patterns in intermediate convolutional layers. In a compositional CNN, each
filter is supposed to consistently represent a specific compositional object
part or image region with a clear meaning. The compositional CNN learns from
image labels for classification without any annotations of parts or regions for
supervision. Our method can be broadly applied to different types of CNNs.
Experiments have demonstrated the effectiveness of our method.
