In this paper, we consider the problem of domain generalization in semantic
segmentation, which aims to learn a robust model using only labeled synthetic
(source) data. The model is expected to perform well on unseen real (target)
domains. Our study finds that the image style variation can largely influence
the model's performance and the style features can be well represented by the
channel-wise mean and standard deviation of images. Inspired by this, we
propose a novel adversarial style augmentation (AdvStyle) approach, which can
dynamically generate hard stylized images during training and thus can
effectively prevent the model from overfitting on the source domain.
Specifically, AdvStyle regards the style feature as a learnable parameter and
updates it by adversarial training. The learned adversarial style feature is
used to construct an adversarial image for robust model training. AdvStyle is
easy to implement and can be readily applied to different models. Experiments
on two synthetic-to-real semantic segmentation benchmarks demonstrate that
AdvStyle can significantly improve the model performance on unseen real domains
and show that we can achieve the state of the art. Moreover, AdvStyle can be
employed to domain generalized image classification and produces a clear
improvement on the considered datasets.

本文提出 Adversarial Style Augmentation 方法来解决语义分割中的领域通用性问题，其通过在训练过程中动态生成样式强化的图像，以有效地避免模型过度拟合于源域，并且在两个合成 - 真实语义分割基准测试数据集上进行实验，结果显示 Adversarial Style Augmentation 可以显着提高模型在未见真实域上的性能。