Invariant approaches have been remarkably successful in tackling the problem
of domain generalization, where the objective is to perform inference on data
distributions different from those used in training. In our work, we
investigate whether it is possible to leverage domain information from the
unseen test samples themselves. We propose a domain-adaptive approach
consisting of two steps: a) we first learn a discriminative domain embedding
from unsupervised training examples, and b) use this domain embedding as
supplementary information to build a domain-adaptive model, that takes both the
input as well as its domain into account while making predictions. For unseen
domains, our method simply uses few unlabelled test examples to construct the
domain embedding. This enables adaptive classification on any unseen domain.
Our approach achieves state-of-the-art performance on various domain
generalization benchmarks. In addition, we introduce the first real-world,
large-scale domain generalization benchmark, Geo-YFCC, containing 1.1M samples
over 40 training, 7 validation, and 15 test domains, orders of magnitude larger
than prior work. We show that the existing approaches either do not scale to
this dataset or underperform compared to the simple baseline of training a
model on the union of data from all training domains. In contrast, our approach
achieves a significant improvement.

本论文提出了基于领域自适应的方法，将无监督训练样本中的领域信息作为辅助信息，采用学习的鉴别性领域嵌入构建领域自适应模型，用于即使在未见过领域上的预测任务。该方法在各种领域泛化基准上均实现了最先进的性能，并引入了首个现实世界的大规模领域泛化基准 Geo-YFCC，相对于现有的方法，该基于领域自适应的方法在该数据集上实现了显着的优化。