We address the task of domain generalization, where the goal is to train a
predictive model such that it is able to generalize to a new, previously unseen
domain. We choose a hierarchical generative approach within the framework of
variational autoencoders and propose a domain-unsupervised algorithm that is
able to generalize to new domains without domain supervision. We show that our
method is able to learn representations that disentangle domain-specific
information from class-label specific information even in complex settings
where domain structure is not observed during training. Our interpretable
method outperforms previously proposed generative algorithms for domain
generalization as well as other non-generative state-of-the-art approaches in
several hierarchical domain settings including sequential overlapped near
continuous domain shift. It also achieves competitive performance on the
standard domain generalization benchmark dataset PACS compared to
state-of-the-art approaches which rely on observing domain-specific information
during training, as well as another domain unsupervised method. Additionally,
we proposed model selection purely based on Evidence Lower Bound (ELBO) and
also proposed weak domain supervision where implicit domain information can be
added into the algorithm.

本文提出了一种基于层次生成方法和变分自编码器的无监督领域泛化算法，能够在不受领域特定信息约束的情况下学习表征并在多种领域设置下优于先前提出的领域泛化算法和现有的其他非生成式方法。另外，作者还提出了基于 ELBO 的模型选择和弱领域监督算法。