  Leveraging datasets available to learn a model with high generalization
ability to unseen domains is important for computer vision, especially when the
unseen domain's annotated data are unavailable. We study a novel and practical
problem of Open Domain Generalization (OpenDG), which learns from different
source domains to achieve high performance on an unknown target domain, where
the distributions and label sets of each individual source domain and the
target domain can be different. The problem can be generally applied to diverse
source domains and widely applicable to real-world applications. We propose a
Domain-Augmented Meta-Learning framework to learn open-domain generalizable
representations. We augment domains on both feature-level by a new Dirichlet
mixup and label-level by distilled soft-labeling, which complements each domain
with missing classes and other domain knowledge. We conduct meta-learning over
domains by designing new meta-learning tasks and losses to preserve domain
unique knowledge and generalize knowledge across domains simultaneously.
Experiment results on various multi-domain datasets demonstrate that the
proposed Domain-Augmented Meta-Learning (DAML) outperforms prior methods for
unseen domain recognition.
