We study the attribution problem [28] for deep networks applied to perception
tasks. For vision tasks, attribution techniques attribute the prediction of a
network to the pixels of the input image. We propose a new technique called
\emph{Blur Integrated Gradients}. This technique has several advantages over
other methods. First, it can tell at what scale a network recognizes an object.
It produces scores in the scale/frequency dimension, that we find captures
interesting phenomena. Second, it satisfies the scale-space axioms [14], which
imply that it employs perturbations that are free of artifact. We therefore
produce explanations that are cleaner and consistent with the operation of deep
networks. Third, it eliminates the need for a 'baseline' parameter for
Integrated Gradients [31] for perception tasks. This is desirable because the
choice of baseline has a significant effect on the explanations. We compare the
proposed technique against previous techniques and demonstrate application on
three tasks: ImageNet object recognition, Diabetic Retinopathy prediction, and
AudioSet audio event identification.

该论文研究应用于感知任务的深度网络的归因问题，并提出了一种名为 “Blur Integrated Gradients” 的新技术，该技术比其他方法具有多个优点，可以准确识别物体并产生一致且干净的解释，适用于图像，视网膜病变和音频事件等多个任务。