Despite remarkable success in a variety of applications, it is well-known
that deep learning can fail catastrophically when presented with
out-of-distribution data. Toward addressing this challenge, we consider the
domain generalization problem, wherein predictors are trained using data drawn
from a family of related training domains and then evaluated on a distinct and
unseen test domain. We show that under a natural model of data generation and a
concomitant invariance condition, the domain generalization problem is
equivalent to an infinite-dimensional constrained statistical learning problem;
this problem forms the basis of our approach, which we call Model-Based Domain
Generalization. Due to the inherent challenges in solving constrained
optimization problems in deep learning, we exploit nonconvex duality theory to
develop unconstrained relaxations of this statistical problem with tight bounds
on the duality gap. Based on this theoretical motivation, we propose a novel
domain generalization algorithm with convergence guarantees. In our
experiments, we report improvements of up to 30 percentage points over
state-of-the-art domain generalization baselines on several benchmarks
including ColoredMNIST, Camelyon17-WILDS, FMoW-WILDS, and PACS.

本篇论文提出了一种基于模型的域泛化方法，通过对数据生成过程和同变性条件的建模，将域泛化问题转化为一个无限维的有约束统计学习问题，并利用非凸对偶理论发展了有约束松弛的统计问题，提出了具有收敛保证的域泛化算法，并在 ColoredMNIST，Camelyon17-WILDS，FMoW-WILDS 和 PACS 等基准测试中取得了高达 30 个百分点的改进。