APPENDIX MADG: Margin-based Adversarial Learning for Domain Generalization

Theorem 2. Consider a mixture of Theorem 3. Given the same setting as Corollary 1 and Lemma 3, for any From Lemma 3, we upper-bound the expected MDD as shown below. Using the above results and Corollary 1, we get Theorem 3 . In this section, we discuss earlier literature proposed for the DG problem, in terms of their broad categories. By conducting experiments on these benchmark datasets, we ensure a comprehensive evaluation of the proposed MADG model's performance, taking into account diverse domains, varying class distributions, and image characteristics. This section provides detailed results for each domain on all five datasets in Tables A3 to A7.