Point cloud data is ubiquitous in scientific fields. Recently, geometric deep learning (GDL) has been widely applied to solve prediction tasks with such data. However, GDL models are often complicated and hardly interpretable, which poses concerns to scientists who are to deploy these models in scientific analysis and experiments. This work proposes a general mechanism, learnable randomness injection (LRI), which allows building inherently interpretable models based on general GDL backbones. LRI-induced models, once trained, can detect the points in the point cloud data that carry information indicative of the prediction label. We also propose four datasets from real scientific applications that cover the domains of high-energy physics and biochemistry to evaluate the LRI mechanism. Compared with previous post-hoc interpretation methods, the points detected by LRI align much better and stabler with the ground-truth patterns that have actual scientific meanings. LRI is grounded by the information bottleneck principle, and thus LRI-induced models are also more robust to distribution shifts between training and test scenarios. Our code and datasets are available at \url{https://github.com/Graph-COM/LRI}.
翻译:最近,几何深深学习(GDL)被广泛应用,用这些数据解决预测任务。然而,GDL模型往往复杂,难以解释,这给在科学分析和实验中部署这些模型的科学家带来了关切。这项工作提出了一个一般机制,即可学习随机喷射(LRI),允许根据一般GDL主干线建立内在可解释的模型。LRI诱导模型一旦经过培训,就可以探测点云数据中的点点,显示预测标签的信息。我们还从实际科学应用中提议四个数据集,涵盖高能物理和生物化学领域,以评价LRI机制。与以往的热后解释方法相比,LRI检测到的点与具有实际科学含义的地面图案模式相匹配得更好和稳定得多。LRI以信息瓶颈原则为基础,因此,LRI诱导模型也可以更有力地在培训和测试假设中进行分布。我们的代码和数据数据集可在 COMl/GIS/GIphu/GRA}。</s>