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.

翻译：域泛化技术已成为应对深度学习领域迁移挑战的主流方法，其目标是对训练中未见的目域实现良好泛化。近年间，针对域泛化问题涌现出大量方法，其中对抗学习范式的方案尤受关注。此类方法的核心思想是通过最小化差异度量来学习域不变特征。然而，当前多数对抗域泛化方法采用基于0-1损失的$\mathcal{H}\Delta\mathcal{H}$散度度量，而基于间隔损失的差异度量具有信息更丰富、界限更紧、更实用且可高效优化等优势。为弥合这一差距，本文提出一种基于间隔损失差异度量的新型对抗学习域泛化算法MADG。该模型通过学习所有源域的域不变特征，并采用对抗训练实现对未见目域的稳健泛化。我们基于未见目域误差上界提供了MADG模型的理论分析：在实值假设空间中构建了源域与未见域之间的关联，并利用间隔损失与Rademacher复杂度推导出泛化界。我们在VLCS、PACS、OfficeHome、DomainNet及TerraIncognita等主流域泛化数据集上对MADG模型进行了充分实验，基于DomainBed基准框架的评估表明该算法在所有数据集上均展现出一致稳定的性能。