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.
翻译:在本文中,我们基于对无形领域风险的新上限,提出了一个新的域通用框架(DG)框架。 特别是,我们的框架建议共同尽量减少共同变换以及被观察领域之间的概念变换,以便在无形领域实现更好的绩效。 虽然拟议方法可以通过任意结合共变调整和概念调整模块加以实施,但在这项工作中,我们使用完善的分布调整方法,即最大平均值差异和共变一致(CORAL),并采用基于常变风险最小化(IRM)的概念调整方法。 我们的数字结果显示,拟议方法在几个数据集上的表现或好于或好于域常规化的最新方法。