  Single domain generalization (single DG) aims at learning a robust model
generalizable to unseen domains from only one training domain, making it a
highly ambitious and challenging task. State-of-the-art approaches have mostly
relied on data augmentations, such as adversarial perturbation and style
enhancement, to synthesize new data and thus increase robustness. Nevertheless,
they have largely overlooked the underlying coherence between the augmented
domains, which in turn leads to inferior results in real-world scenarios. In
this paper, we propose a simple yet effective scheme, termed as
\emph{StyDeSty}, to explicitly account for the alignment of the source and
pseudo domains in the process of data augmentation, enabling them to interact
with each other in a self-consistent manner and further giving rise to a latent
domain with strong generalization power. The heart of StyDeSty lies in the
interaction between a \emph{stylization} module for generating novel stylized
samples using the source domain, and a \emph{destylization} module for
transferring stylized and source samples to a latent domain to learn
content-invariant features. The stylization and destylization modules work
adversarially and reinforce each other. During inference, the destylization
module transforms the input sample with an arbitrary style shift to the latent
domain, in which the downstream tasks are carried out. Specifically, the
location of the destylization layer within the backbone network is determined
by a dedicated neural architecture search (NAS) strategy. We evaluate StyDeSty
on multiple benchmarks and demonstrate that it yields encouraging results,
outperforming the state of the art by up to {13.44%} on classification
accuracy. Codes are available here: https://github.com/Huage001/StyDeSty.
