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.

翻译：输入扰动方法将部分输入到函数中, 并测量函数输出的变化。 最近, 输入扰动方法被用于生成和评估来自进化神经网络的显著地图。 在实践中, 中性基线图像用于隔离, 因此基准图像对分类概率的影响微乎其微。 但是, 在本文中, 我们显示, 可以说中性的基线图像仍然影响生成的显著地图, 并用输入扰动来评估这些图像。 我们还表明, 许多选择的超参数导致输入扰动生成的突出地图的差异。 我们实验性地揭示了输入扰动方法中的一些不一致之处, 发现这些图像在生成显著地图和评估突出的特征地图时缺乏坚固性。