We consider the problem of domain generalization, in which a predictor is trained on data drawn from a family of related training domains and tested on a distinct and unseen test domain. While a variety of approaches have been proposed for this setting, it was recently shown that no existing algorithm can consistently outperform empirical risk minimization (ERM) over the training domains. To this end, in this paper we propose a novel approach for the domain generalization problem called Model-Based Domain Generalization. In our approach, we first use unlabeled data from the training domains to learn multi-modal domain transformation models that map data from one training domain to any other domain. Next, we propose a constrained optimization-based formulation for domain generalization which enforces that a trained predictor be invariant to distributional shifts under the underlying domain transformation model. Finally, we propose a novel algorithmic framework for efficiently solving this constrained optimization problem. In our experiments, we show that this approach outperforms both ERM and domain generalization algorithms on numerous well-known, challenging datasets, including WILDS, PACS, and ImageNet. In particular, our algorithms beat the current state-of-the-art methods on the very-recently-proposed WILDS benchmark by up to 20 percentage points.

中文翻译：

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基于模型的领域概括

我们考虑域泛化的问题，其中对预测变量进行训练，该预测变量是从一组相关的训练域中提取的数据进行训练的，并在不同且看不见的测试域中进行测试。尽管已针对此设置提出了多种方法，但最近显示，在训练域上，没有任何现有算法能够始终胜过经验风险最小化（ERM）。为此，在本文中，我们提出了一种针对域泛化问题的新方法，称为基于模型的域泛化。在我们的方法中，我们首先使用来自训练域的未标记数据来学习多模式域转换模型，该模型将数据从一个训练域映射到任何其他域。下一个，我们提出了一种基于约束优化的域泛化公式，该公式强制要求训练后的预测变量对基础域转换模型下的分布移位是不变的。最后，我们提出了一种新颖的算法框架，可以有效地解决该约束优化问题。在我们的实验中，我们证明了这种方法在众多著名的，具有挑战性的数据集（包括WILDS，PACS和ImageNet）上均优于ERM和领域通用算法。尤其是，我们的算法在最近提出的WILDS基准测试中比当前最新方法高出20个百分点。我们证明，这种方法在众多著名的，具有挑战性的数据集（包括WILDS，PACS和ImageNet）上均优于ERM和领域通用化算法。尤其是，我们的算法在最近提出的WILDS基准测试中比当前最新方法高出20个百分点。我们证明，这种方法在众多著名的，具有挑战性的数据集（包括WILDS，PACS和ImageNet）上均优于ERM和领域通用化算法。尤其是，我们的算法在最近提出的WILDS基准测试中比当前最新方法高出20个百分点。