  Domain generalization aims to enhance the model robustness against domain
shift without accessing the target domain. Since the available source domains
for training are limited, recent approaches focus on generating samples of
novel domains. Nevertheless, they either struggle with the optimization problem
when synthesizing abundant domains or cause the distortion of class semantics.
To these ends, we propose a novel domain generalization framework where feature
statistics are utilized for stylizing original features to ones with novel
domain properties. To preserve class information during stylization, we first
decompose features into high and low frequency components. Afterward, we
stylize the low frequency components with the novel domain styles sampled from
the manipulated statistics, while preserving the shape cues in high frequency
ones. As the final step, we re-merge both components to synthesize novel domain
features. To enhance domain robustness, we utilize the stylized features to
maintain the model consistency in terms of features as well as outputs. We
achieve the feature consistency with the proposed domain-aware supervised
contrastive loss, which ensures domain invariance while increasing class
discriminability. Experimental results demonstrate the effectiveness of the
proposed feature stylization and the domain-aware contrastive loss. Through
quantitative comparisons, we verify the lead of our method upon existing
state-of-the-art methods on two benchmarks, PACS and Office-Home.
