We propose to study and promote the robustness of a model as per its performance through the interpolation of training data distributions. Specifically, (1) we augment the data by finding the worst-case Wasserstein barycenter on the geodesic connecting subpopulation distributions of different categories. (2) We regularize the model for smoother performance on the continuous geodesic path connecting subpopulation distributions. (3) Additionally, we provide a theoretical guarantee of robustness improvement and investigate how the geodesic location and the sample size contribute, respectively. Experimental validations of the proposed strategy on four datasets, including CIFAR-100 and ImageNet, establish the efficacy of our method, e.g., our method improves the baselines' certifiable robustness on CIFAR10 up to $7.7\%$, with $16.8\%$ on empirical robustness on CIFAR-100. Our work provides a new perspective of model robustness through the lens of Wasserstein geodesic-based interpolation with a practical off-the-shelf strategy that can be combined with existing robust training methods.
翻译:我们提议通过培训数据分布的内插来研究并促进模型的稳健性,具体来说,(1) 我们通过在不同类别亚人口分布的大地测量联系中找到最差的瓦西斯坦温温热点来增加数据;(2) 我们将连接亚人口分布的连续大地测量路径的顺畅性能模式正规化。(3) 此外,我们还从理论上保证强性改善,并调查大地测量位置和样本大小如何分别发挥作用。对四个数据集,包括CIFAR-100和图像网络的拟议战略的实验性验证,确定了我们方法的功效,例如,我们的方法将CIFAR10的基线的可验证稳性提高到7.7美元,其中16.8美元是连接亚人口分布的实证性强性。我们的工作通过瓦西斯坦以大地测量为基础的内推法和可与现有稳健的培训方法相结合的实用的现成战略,为模型的稳健性提供了一个新的视角。