We study the problem of explaining a rich class of behavioral properties of
deep neural networks. Distinctively, our influence-directed explanations
approach this problem by peering inside the network to identify neurons with
high influence on a quantity and distribution of interest, using an
axiomatically-justified influence measure, and then providing an interpretation
for the concepts these neurons represent. We evaluate our approach by
demonstrating a number of its unique capabilities on convolutional neural
networks trained on ImageNet. Our evaluation demonstrates that
influence-directed explanations (1) identify influential concepts that
generalize across instances, (2) can be used to extract the "essence" of what
the network learned about a class, and (3) isolate individual features the
network uses to make decisions and distinguish related classes.

该研究旨在解释深度神经网络的行为特性，作者通过使用基于公理的影响测量方法来识别对感兴趣的数量和分布具有高影响力的神经元，并解释这些神经元所代表的概念。通过在 ImageNet 数据集上测试，作者证明了影响导向的解释方法具有将信仰术语的影响概念推广到更多实例的能力，可以提取网络中关于类别的核心信息，可以分离网络用于做出决策和区分相关类别所使用的单个特征。