In real-world applications, a machine learning model is required to handle an open-set recognition (OSR), where unknown classes appear during the inference, in addition to a domain shift, where the distribution of data differs between the training and inference phases. Domain generalization (DG) aims to handle the domain shift situation where the target domain of the inference phase is inaccessible during model training. Open domain generalization (ODG) takes into account both DG and OSR. Domain-Augmented Meta-Learning (DAML) is a method targeting ODG but has a complicated learning process. On the other hand, although various DG methods have been proposed, they have not been evaluated in ODG situations. This work comprehensively evaluates existing DG methods in ODG and shows that two simple DG methods, CORrelation ALignment (CORAL) and Maximum Mean Discrepancy (MMD), are competitive with DAML in several cases. In addition, we propose simple extensions of CORAL and MMD by introducing the techniques used in DAML, such as ensemble learning and Dirichlet mixup data augmentation. The experimental evaluation demonstrates that the extended CORAL and MMD can perform comparably to DAML with lower computational costs. This suggests that the simple DG methods and their simple extensions are strong baselines for ODG. The code used in the experiments is available at https://github.com/shiralab/OpenDG-Eval.

翻译：在现实应用中，机器学习模型需要处理开放集识别（OSR）——推理阶段会出现未知类别，同时还要应对域偏移——即训练阶段与推理阶段的数据分布存在差异。领域泛化（DG）旨在处理目标域在模型训练期间不可访问时的域偏移情况。开放域泛化（ODG）同时考虑了DG和OSR。域增强元学习（DAML）是一种针对ODG的方法，但其学习过程较为复杂。另一方面，尽管已有多种DG方法被提出，但它们在ODG场景中尚未得到评估。本研究全面评估了现有DG方法在ODG中的表现，并表明两种简单的DG方法——相关对齐（CORAL）和最大均值差异（MMD）——在多种情况下与DAML具有竞争力。此外，我们通过引入DAML中使用的技术（如集成学习和狄利克雷混合数据增强）提出了CORAL和MMD的简单扩展。实验评估表明，扩展后的CORAL和MMD能够在更低计算成本下达到与DAML相当的性能。这表明简单的DG方法及其简单扩展是ODG的强基线方法。实验所用代码可从https://github.com/shiralab/OpenDG-Eval 获取。