We focus on Unsupervised Domain Adaptation (UDA) for the task of semantic
segmentation. Recently, adversarial alignment has been widely adopted to match
the marginal distribution of feature representations across two domains
globally. However, this strategy fails in adapting the representations of the
tail classes or small objects for semantic segmentation since the alignment
objective is dominated by head categories or large objects. In contrast to
adversarial alignment, we propose to explicitly train a domain-invariant
classifier by generating and defensing against pointwise feature space
adversarial perturbations. Specifically, we firstly perturb the intermediate
feature maps with several attack objectives (i.e., discriminator and
classifier) on each individual position for both domains, and then the
classifier is trained to be invariant to the perturbations. By perturbing each
position individually, our model treats each location evenly regardless of the
category or object size and thus circumvents the aforementioned issue.
Moreover, the domain gap in feature space is reduced by extrapolating source
and target perturbed features towards each other with attack on the domain
discriminator. Our approach achieves the state-of-the-art performance on two
challenging domain adaptation tasks for semantic segmentation: GTA5 ->
Cityscapes and SYNTHIA -> Cityscapes.

该研究论文提出了一种利用点对点特征空间敌对扰动来显式训练领域不变分类器的方法，进而解决领域间较小或头部类别在语义分割中支配对齐目标的问题，并在 GAT5 -> Cityscapes 和 SYNTHIA -> Cityscapes 两个语义分割挑战领域中取得了最新的最佳表现。