A fundamental challenge for machine learning models is generalizing to
out-of-distribution (OOD) data, in part due to spurious correlations. To tackle
this challenge, we first formalize the OOD generalization problem as
constrained optimization, called Disentanglement-constrained Domain
Generalization (DDG). We relax this non-trivial constrained optimization
problem to a tractable form with finite-dimensional parameterization and
empirical approximation. Then a theoretical analysis of the extent to which the
above transformations deviates from the original problem is provided. Based on
the transformation, we propose a primal-dual algorithm for joint representation
disentanglement and domain generalization. In contrast to traditional
approaches based on domain adversarial training and domain labels, DDG jointly
learns semantic and variation encoders for disentanglement, enabling flexible
manipulation and augmentation on training data. DDG aims to learn intrinsic
representations of semantic concepts that are invariant to nuisance factors and
generalizable across domains. Comprehensive experiments on popular benchmarks
show that DDG can achieve competitive OOD performance and uncover interpretable
salient structures within data.

提出了一种名为 DDG 的方法，利用基于约束的优化形式来处理机器学习模型的泛化问题，并以有限维参数化和经验逼近的方式对其进行简化，同时提出了一种基于原始对偶算法来实现表示分离和域泛化的方法。DDG 方法可以学习到语义概念的内在表示，使其对干扰因素具有不变性，并可以在多个领域中得到良好的 OOU 性能表现。