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.

本研究旨在利用可解释性方法和系统性实验探究在操作点云的深层模型中，如何应对由少量判别性输入变量与目标函数关联所导致的泛化能力下降，提出了 PointMask 方法，该方法通过最小化输入与潜在特征之间的互信息来屏蔽不相关变量，进而在与任意模型中结合使用时有助于预测分数大的区域的解释性，并证明该方法能够有效处理数据偏差。

PointMask: 面向可解释和偏差鲁棒的点云处理

PointMask: Towards Interpretable and Bias-Resilient Point Cloud  Processing

We propose a novel approach to achieving invariance for deep neural networks
in the form of inducing amnesia to unwanted factors of data through a new
adversarial forgetting mechanism. We show that the forgetting mechanism serves
as an information-bottleneck, which is manipulated by the adversarial training
to learn invariance to unwanted factors. Empirical results show that the
proposed framework achieves state-of-the-art performance at learning invariance
in both nuisance and bias settings on a diverse collection of datasets and
tasks.

本论文提供了一种新方法，通过对抗遗忘机制来实现深度神经网络的不变性，使其可以在多种数据集和任务中实现状态 - of-the-art 的性能。