Influence-Directed Explanations for Deep Convolutional Networks
We study the problem of explaining a rich class of behavioral properties of deep neural networks.Our influence-directed explanations approach this problem by peering inside the network to identify neurons with high influence on the property of interest using an axiomatically justified influence measure, and then providing an interpretation for the concepts these neurons represent.We evaluate our approach by training convolutional neural networks on Pubfig, ImageNet, and Diabetic Retinopathy datasets. Our evaluation demonstrates that influence-directed explanations localize features used by the network, isolate features distinguishing related instances, help extract the essence of what the network learned about the class, and assist in debugging misclassifications.
We present an influence-directed approach to constructing explanations for the behavior of deep convolutional networks, and show how it can be used to answer a broad set of questions that could not be addressed by prior work.A way to measure influence that satisfies certain axioms, and a notion of influence that can be used to identify what input part is most influential for the output of a neuron in a deep neural network.This paper proposes to measure the influence of single neurons with regard to a quantity of interest represented by another neuron.