Recent progress in empirical and certified robustness promises to deliver
reliable and deployable Deep Neural Networks (DNNs). Despite that success, most
existing evaluations of DNN robustness have been done on images sampled from
the same distribution on which the model was trained. However, in the real
world, DNNs may be deployed in dynamic environments that exhibit significant
distribution shifts. In this work, we take a first step towards thoroughly
investigating the interplay between empirical and certified adversarial
robustness on one hand and domain generalization on another. To do so, we train
robust models on multiple domains and evaluate their accuracy and robustness on
an unseen domain. We observe that: (1) both empirical and certified robustness
generalize to unseen domains, and (2) the level of generalizability does not
correlate well with input visual similarity, measured by the FID between source
and target domains. We also extend our study to cover a real-world medical
application, in which adversarial augmentation significantly boosts the
generalization of robustness with minimal effect on clean data accuracy.

本研究探讨了基于域一般化的经验和认证鲁棒性之间的相互影响，并发现两种鲁棒性均适用于未知的数据域。同时我们也证明，在医学应用中，对抗性增强显著提高了鲁棒性的泛化能力，对数据准确性影响不大。

对于分布转移下的对抗鲁棒性的通用性

Generalizability of Adversarial Robustness Under Distribution Shifts

Protein complex formation is a central problem in biology, being involved in
most of the cell's processes, and essential for applications, e.g. drug design
or protein engineering. We tackle rigid body protein-protein docking, i.e.,
computationally predicting the 3D structure of a protein-protein complex from
the individual unbound structures, assuming no conformational change within the
proteins happens during binding. We design a novel pairwise-independent
SE(3)-equivariant graph matching network to predict the rotation and
translation to place one of the proteins at the right docked position relative
to the second protein. We mathematically guarantee a basic principle: the
predicted complex is always identical regardless of the initial locations and
orientations of the two structures. Our model, named EquiDock, approximates the
binding pockets and predicts the docking poses using keypoint matching and
alignment, achieved through optimal transport and a differentiable Kabsch
algorithm. Empirically, we achieve significant running time improvements and
often outperform existing docking software despite not relying on heavy
candidate sampling, structure refinement, or templates.

本文通过设计 SE（3）- 等变图匹配网络，使用关键点匹配和对齐来逼近配体口袋，并使用可微分 Kabsch 算法预测对接姿态，从而预测蛋白质复合物的 3D 结构，提出了一个名为 EquiDock 的模型。该模型在不需要大量采样、结构精炼或模板的情况下，在大多数情况下都能在计算时间上优于现有的对接软件，但仍需要进行实证研究。