  Domain Generalization (DG) techniques have emerged as a popular approach to
address the challenges of domain shift in Deep Learning (DL), with the goal of
generalizing well to the target domain unseen during the training. In recent
years, numerous methods have been proposed to address the DG setting, among
which one popular approach is the adversarial learning-based methodology. The
main idea behind adversarial DG methods is to learn domain-invariant features
by minimizing a discrepancy metric. However, most adversarial DG methods use
0-1 loss based $\mathcal{H}\Delta\mathcal{H}$ divergence metric. In contrast,
the margin loss-based discrepancy metric has the following advantages: more
informative, tighter, practical, and efficiently optimizable. To mitigate this
gap, this work proposes a novel adversarial learning DG algorithm, MADG,
motivated by a margin loss-based discrepancy metric. The proposed MADG model
learns domain-invariant features across all source domains and uses adversarial
training to generalize well to the unseen target domain. We also provide a
theoretical analysis of the proposed MADG model based on the unseen target
error bound. Specifically, we construct the link between the source and unseen
domains in the real-valued hypothesis space and derive the generalization bound
using margin loss and Rademacher complexity. We extensively experiment with the
MADG model on popular real-world DG datasets, VLCS, PACS, OfficeHome,
DomainNet, and TerraIncognita. We evaluate the proposed algorithm on
DomainBed's benchmark and observe consistent performance across all the
datasets.
