  Unsupervised Domain Adaptation (UDA) aims at improving the generalization
capability of a model trained on a source domain to perform well on a target
domain for which no labeled data is available. In this paper, we consider the
semantic segmentation of urban scenes and we propose an approach to adapt a
deep neural network trained on synthetic data to real scenes addressing the
domain shift between the two different data distributions. We introduce a novel
UDA framework where a standard supervised loss on labeled synthetic data is
supported by an adversarial module and a self-training strategy aiming at
aligning the two domain distributions. The adversarial module is driven by a
couple of fully convolutional discriminators dealing with different domains:
the first discriminates between ground truth and generated maps, while the
second between segmentation maps coming from synthetic or real world data. The
self-training module exploits the confidence estimated by the discriminators on
unlabeled data to select the regions used to reinforce the learning process.
Furthermore, the confidence is thresholded with an adaptive mechanism based on
the per-class overall confidence. Experimental results prove the effectiveness
of the proposed strategy in adapting a segmentation network trained on
synthetic datasets like GTA5 and SYNTHIA, to real world datasets like
Cityscapes and Mapillary.
