  Due to the absence of ground truth, objective evaluation of explainability
methods is an essential research direction. So far, the vast majority of
evaluations can be summarized into three categories, namely human evaluation,
sensitivity testing, and salinity check. This work proposes a novel evaluation
methodology from the perspective of generalizability. We employ an Autoencoder
to learn the distributions of the generated explanations and observe their
learnability as well as the plausibility of the learned distributional
features. We first briefly demonstrate the evaluation idea of the proposed
approach at LIME, and then quantitatively evaluate multiple popular
explainability methods. We also find that smoothing the explanations with
SmoothGrad can significantly enhance the generalizability of explanations.
