  Domain adaptation (DA) and domain generalization (DG) have emerged as a
solution to the domain shift problem where the distribution of the source and
target data is different. The task of DG is more challenging than DA as the
target data is totally unseen during the training phase in DG scenarios. The
current state-of-the-art employs adversarial techniques, however, these are
rarely considered for the DG problem. Furthermore, these approaches do not
consider correlation alignment which has been proven highly beneficial for
minimizing domain discrepancy. In this paper, we propose a correlation-aware
adversarial DA and DG framework where the features of the source and target
data are minimized using correlation alignment along with adversarial learning.
Incorporating the correlation alignment module along with adversarial learning
helps to achieve a more domain agnostic model due to the improved ability to
reduce domain discrepancy with unlabeled target data more effectively.
Experiments on benchmark datasets serve as evidence that our proposed method
yields improved state-of-the-art performance.
