  Deep classifiers tend to associate a few discriminative input variables with
their objective function, which in turn, may hurt their generalization
capabilities. To address this, one can design systematic experiments and/or
inspect the models via interpretability methods. In this paper, we investigate
both of these strategies on deep models operating on point clouds. We propose
PointMask, a model-agnostic interpretable information-bottleneck approach for
attribution in point cloud models. PointMask encourages exploring the majority
of variation factors in the input space while gradually converging to a general
solution. More specifically, PointMask introduces a regularization term that
minimizes the mutual information between the input and the latent features used
to masks out irrelevant variables. We show that coupling a PointMask layer with
an arbitrary model can discern the points in the input space which contribute
the most to the prediction score, thereby leading to interpretability. Through
designed bias experiments, we also show that thanks to its gradual masking
feature, our proposed method is effective in handling data bias.
