  In recent years, unsupervised domain adaptation (UDA) for semantic
segmentation has brought many researchers'attention. Many of them take an
approach to design a complex system so as to better align the gap between
source and target domain. Instead, we focus on the very basic structure of the
deep neural network, Batch Normalization, and propose to replace the Sharing
Affine Transformation with our proposed Separate Affine Transformation (SEAT).
The proposed SEAT is simple, easily implemented and easy to integrate into
existing adversarial learning based UDA methods. Also, to further improve the
adaptation quality, we introduce multi level adaptation by adding the
lower-level features to the higher-level ones before feeding them to the
discriminator, without adding extra discriminator like others. Experiments show
that the proposed methods is less complex without losing performance accuracy
when compared with other UDA methods.
