Abstract: In recent years,with the popularity of artificial intelligence in various fields,it has become an increasingly important topic to study the interpretable methods of neural networks and understand their running principles.As a subfield of neural network interpretability methods,the interpretability of network pathways garners increasing attention.This paper particularly focuses on the critical data routing path(CDRP),an interpretable method for network pathways.Firstly,the routing path visualization attribution of CDRP in the input domain is analyzed by use of the score-class activation map(Score-CAM) method,pointing out the inherent defects of the CDRP approach in terms of semantics.Then a channel semantic guided CDRP method termed as Score-CDRP is proposed,which improves the semantic consistency between the original deep neural network and its corresponding CDRP from the perspective of method mechanism.Lastly,experimental results demonstrate that the proposed Score-CDRP approach is more reasonable,effective and robust than CDRP in terms of visualization of the routing path heatmap as well as its corresponding prediction and localization accuracy.

Key words: Computer vision, Deep neural networks, Interpretability of neural networks, Feature visualization, Network pruning, Heatmap