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.

翻译：尽管在标准学习环境中非常强大,但是深层学习模式如果在与培训对象不同的情景中部署,可能会极其脆弱。域通用方法调查这一问题,数据增强战略已证明是增加数据可变性的有用工具,支持跨领域模式的稳健性。在我们的工作中,我们侧重于风格传输数据扩增,我们介绍了如何用简单和廉价的战略来实施数据扩增。此外,我们分析了与增强解决方案相结合时最新水平的域域通用方法的行为:我们彻底的实验评估表明,在扩展基线方面,其原始效果几乎总是消失。这为域通用研究提供了新情景,凸显了适当利用引入的数据可变性的新方法的必要性。