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Understanding the predictions made by Artificial Intelligence (AI) systems isbecoming more and more important as deep learning models are used forincreasingly complex and high-stakes tasks. Saliency mapping -- a popularvisual attribution method -- is one important tool for this, but existingformulations are limited by either computational cost or architecturalconstraints. We therefore propose Hierarchical Perturbation, a very fast andcompletely model-agnostic method for interpreting model predictions with robustsaliency maps. Using standard benchmarks and datasets, we show that oursaliency maps are of competitive or superior quality to those generated byexisting model-agnostic methods -- and are over 20 times faster to compute.

Believe The HiPe: Hierarchical Perturbation for Fast, Robust, and Model-Agnostic Saliency Mapping