PointMask: Towards Interpretable and Bias-Resilient Point Cloud Processing
This addresses interpretability and bias resilience in point cloud models, which is incremental as it builds on existing interpretability methods.
The paper tackles the problem of deep classifiers overfitting to a few discriminative input variables in point cloud processing, which harms generalization, by proposing PointMask, a model-agnostic interpretable information-bottleneck approach that identifies key points for predictions and handles data bias, showing effectiveness in bias experiments.
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.