Attribution in Scale and Space

Shawn Xu
Subhashini Venugopalan


We study the attribution problem (cf. ~\cite{SVZ13}) for deep networks applied to \emph{perception tasks}. Traditionally, the attribution problem is formulated as blaming the network's prediction on the pixels of the input image, i.e., the \emph{space} dimension. Often, signal is also present in the \emph{scale/frequency} dimension. We propose a new technique called \emph{Blur Integrated Gradients} that produces attributions in both space and in scale. Furthermore, we use the scale-space axioms (cf.~\cite{Lindeberg}) to argue that the input perturbations used by Blur Integrated Gradients will not accidentally create features. There resulting explanations are cleaner, and more faithful to how deep networks operate. We compare against some previously proposed techniques and demonstrate applications on three tasks: ImageNet object recognition, Diabetic Retinopathy prediction, and AudioSet audio event identification.