Domain generalization algorithms use training data from multiple domains to
learn models that generalize well to unseen domains. While recently proposed
benchmarks demonstrate that most of the existing algorithms do not outperform
simple baselines, the established evaluation methods fail to expose the impact
of various factors that contribute to the poor performance. In this paper we
propose an evaluation framework for domain generalization algorithms that
allows decomposition of the error into components capturing distinct aspects of
generalization. Inspired by the prevalence of algorithms based on the idea of
domain-invariant representation learning, we extend the evaluation framework to
capture various types of failures in achieving invariance. We show that the
largest contributor to the generalization error varies across methods,
datasets, regularization strengths and even training lengths. We observe two
problems associated with the strategy of learning domain-invariant
representations. On Colored MNIST, most domain generalization algorithms fail
because they reach domain-invariance only on the training domains. On
Camelyon-17, domain-invariance degrades the quality of representations on
unseen domains. We hypothesize that focusing instead on tuning the classifier
on top of a rich representation can be a promising direction.

本文提出了一个可以将错误分解成不同泛化方面的组成部分的域泛化算法评估框架，并将其扩展到捕捉实现不变性的各种失败类型。作者表明，域不变表示学习的策略存在两个问题，并提出了一个可行的调整分类器的方向。通过对 Colored MNIST 和 Camelyon-17 数据集的评估，作者发现泛化误差的最大贡献因素因方法、数据集、正则化强度甚至训练长度而异。