  The problem of domain generalization is to learn from multiple training
domains, and extract a domain-agnostic model that can then be applied to an
unseen domain. Domain generalization (DG) has a clear motivation in contexts
where there are target domains with distinct characteristics, yet sparse data
for training. For example recognition in sketch images, which are distinctly
more abstract and rarer than photos. Nevertheless, DG methods have primarily
been evaluated on photo-only benchmarks focusing on alleviating the dataset
bias where both problems of domain distinctiveness and data sparsity can be
minimal. We argue that these benchmarks are overly straightforward, and show
that simple deep learning baselines perform surprisingly well on them. In this
paper, we make two main contributions: Firstly, we build upon the favorable
domain shift-robust properties of deep learning methods, and develop a low-rank
parameterized CNN model for end-to-end DG learning. Secondly, we develop a DG
benchmark dataset covering photo, sketch, cartoon and painting domains. This is
both more practically relevant, and harder (bigger domain shift) than existing
benchmarks. The results show that our method outperforms existing DG
alternatives, and our dataset provides a more significant DG challenge to drive
future research.
