Evaluating Input Perturbation Methods for Interpreting CNNs and Saliency Map Comparison

TL;DR

This paper critically evaluates input perturbation methods for generating and assessing saliency maps in CNNs, revealing their sensitivity to baseline choices and hyperparameters, and highlighting their lack of robustness.

Contribution

It demonstrates that baseline images and hyperparameter choices significantly affect saliency maps, exposing inconsistencies and robustness issues in current input perturbation methods.

Findings

Baseline images influence saliency maps despite being neutral.

Hyperparameter choices can cause divergence in saliency maps.

Input perturbation methods show inconsistency and lack robustness.

Abstract

Input perturbation methods occlude parts of an input to a function and measure the change in the function's output. Recently, input perturbation methods have been applied to generate and evaluate saliency maps from convolutional neural networks. In practice, neutral baseline images are used for the occlusion, such that the baseline image's impact on the classification probability is minimal. However, in this paper we show that arguably neutral baseline images still impact the generated saliency maps and their evaluation with input perturbations. We also demonstrate that many choices of hyperparameters lead to the divergence of saliency maps generated by input perturbations. We experimentally reveal inconsistencies among a selection of input perturbation methods and find that they lack robustness for generating saliency maps and for evaluating saliency maps as saliency metrics.