  Despite superior performance in many situations, deep neural networks are
often vulnerable to adversarial examples and distribution shifts, limiting
model generalization ability in real-world applications. To alleviate these
problems, recent approaches leverage distributional robustness optimization
(DRO) to find the most challenging distribution, and then minimize loss
function over this most challenging distribution. Regardless of achieving some
improvements, these DRO approaches have some obvious limitations. First, they
purely focus on local regularization to strengthen model robustness, missing a
global regularization effect which is useful in many real-world applications
(e.g., domain adaptation, domain generalization, and adversarial machine
learning). Second, the loss functions in the existing DRO approaches operate in
only the most challenging distribution, hence decouple with the original
distribution, leading to a restrictive modeling capability. In this paper, we
propose a novel regularization technique, following the veins of
Wasserstein-based DRO framework. Specifically, we define a particular joint
distribution and Wasserstein-based uncertainty, allowing us to couple the
original and most challenging distributions for enhancing modeling capability
and applying both local and global regularizations. Empirical studies on
different learning problems demonstrate that our proposed approach
significantly outperforms the existing regularization approaches in various
domains: semi-supervised learning, domain adaptation, domain generalization,
and adversarial machine learning.
