  The emerging field of Explainable Artificial Intelligence focuses on
researching methods of explaining the decision making processes of complex
machine learning models. In the field of explainability for Computer Vision,
explanations are provided as saliency maps, which visualize the importance of
individual pixels of the input w.r.t. the model's prediction. In this work we
focus on a perturbation-based, model-agnostic explainability method called
RISE, elaborate on observed shortcomings of its grid-based approach and propose
two modifications: replacement of square occlusions with convex polygonal
occlusions based on cells of a Voronoi mesh and addition of an informativeness
guarantee to the occlusion mask generator. These modifications, collectively
called VRISE (Voronoi-RISE), are meant to, respectively, improve the accuracy
of maps generated using large occlusions and accelerate convergence of saliency
maps in cases where sampling density is either very low or very high. We
perform a quantitative comparison of accuracy of saliency maps produced by
VRISE and RISE on the validation split of ILSVRC2012, using a saliency-guided
content insertion/deletion metric and a localization metric based on bounding
boxes. Additionally, we explore the space of configurable occlusion pattern
parameters to better understand their influence on saliency maps produced by
RISE and VRISE. We also describe and demonstrate two effects observed over the
course of experimentation, arising from the random sampling approach of RISE:
"feature slicing" and "saliency misattribution". Our results show that convex
polygonal occlusions yield more accurate maps for coarse occlusion meshes and
multi-object images, but improvement is not guaranteed in other cases. The
informativeness guarantee is shown to increase the convergence rate without
incurring a significant computational overhead.
