  Perturbation-based explanation methods often measure the contribution of an
input feature to an image classifier's outputs by heuristically removing it via
e.g. blurring, adding noise, or graying out, which often produce unrealistic,
out-of-samples. Instead, we propose to integrate a generative inpainter into
three representative attribution methods to remove an input feature. Our
proposed change improved all three methods in (1) generating more plausible
counterfactual samples under the true data distribution; (2) being more
accurate according to three metrics: object localization, deletion, and
saliency metrics; and (3) being more robust to hyperparameter changes. Our
findings were consistent across both ImageNet and Places365 datasets and two
different pairs of classifiers and inpainters.
