Many robustness techniques (CORAL, adversarial training, IRM, metric learning) are different ways of solving the same problem: identifying and regularizing against label-preserving variations in your data.
This paper unifies seemingly separate robustness problems (domain adaptation, adversarial training, compositional generalization) under one framework: regularizing neural network gradients to match the covariance of label-preserving variations in deployment data.