  We propose a normalization layer for unsupervised domain adaption in semantic
scene segmentation. Normalization layers are known to improve convergence and
generalization and are part of many state-of-the-art fully-convolutional neural
networks. We show that conventional normalization layers worsen the performance
of current Unsupervised Adversarial Domain Adaption (UADA), which is a method
to improve network performance on unlabeled datasets and the focus of our
research. Therefore, we propose a novel Domain Agnostic Normalization layer and
thereby unlock the benefits of normalization layers for unsupervised
adversarial domain adaptation. In our evaluation, we adapt from the synthetic
GTA5 data set to the real Cityscapes data set, a common benchmark experiment,
and surpass the state-of-the-art. As our normalization layer is domain agnostic
at test time, we furthermore demonstrate that UADA using Domain Agnostic
Normalization improves performance on unseen domains, specifically on
Apolloscape and Mapillary.
