论文标题
联合协变量和概念对准：领域概括的框架
Joint covariate-alignment and concept-alignment: a framework for domain generalization
论文作者
论文摘要
在本文中，我们提出了一个新的领域概括（DG）框架，该框架基于一个新的上层界限，与看不见的领域的风险。尤其是，我们的框架建议共同最大程度地减少可见域之间的协变量偏移以及概念转移，从而在看不见的域上表现更好。虽然可以通过协方差和概念对准模块的任意组合来实施提出的方法，但在这项工作中，我们使用完善的分配对准方法，即最大平均差异（MMD）和协方差差异（MMD）和协方差一致性（珊瑚），并使用不规则的风险（IRM）（IRM）的方法来获得概念。我们的数值结果表明，所提出的方法在多个数据集上的域概括性要比最先进的方法执行或更好。
In this paper, we propose a novel domain generalization (DG) framework based on a new upper bound to the risk on the unseen domain. Particularly, our framework proposes to jointly minimize both the covariate-shift as well as the concept-shift between the seen domains for a better performance on the unseen domain. While the proposed approach can be implemented via an arbitrary combination of covariate-alignment and concept-alignment modules, in this work we use well-established approaches for distributional alignment namely, Maximum Mean Discrepancy (MMD) and covariance Alignment (CORAL), and use an Invariant Risk Minimization (IRM)-based approach for concept alignment. Our numerical results show that the proposed methods perform as well as or better than the state-of-the-art for domain generalization on several data sets.
