一种语义引导的神经网络关键数据路由路径算法

Semantic-guided Neural Network Critical Data Routing Path

近年来,由于人工智能在各领域的普及,研究神经网络的可解释方法及理解神经网络的运作机理已经成为一个愈发重要的话题。作为神经网络解释性方法的一个分支,网络的路径可解释性受到了越来越多的关注。文中特别探讨了关键数据路由路径(Critical Data Routing Path,CDRP)这一面向网络路径的可解释方法。首先,通过Score-CAM(Score-Class Activation Map)方法分析了CDRP在输入域上的路径可视化归因,指出CDRP方法在语义层面的潜在缺陷。然后,提出了一种语义引导的Score-CDRP方法,从方法机理上提升了CDRP与原始神经网络的语义一致性。最后,通过实验从路径热力图可视化以及相应的预测与定位精度等角度验证了Score-CDRP方法相较于CDRP的合理性、有效性和鲁棒性。

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.