  Despite being very powerful in standard learning settings, deep learning
models can be extremely brittle when deployed in scenarios different from those
on which they were trained. Domain generalization methods investigate this
problem and data augmentation strategies have shown to be helpful tools to
increase data variability, supporting model robustness across domains. In our
work we focus on style transfer data augmentation and we present how it can be
implemented with a simple and inexpensive strategy to improve generalization.
Moreover, we analyze the behavior of current state of the art domain
generalization methods when integrated with this augmentation solution: our
thorough experimental evaluation shows that their original effect almost always
disappears with respect to the augmented baseline. This issue open new
scenarios for domain generalization research, highlighting the need of novel
methods properly able to take advantage of the introduced data variability.
