  Semantic segmentation models based on convolutional neural networks have
recently displayed remarkable performance for a multitude of applications.
However, these models typically do not generalize well when applied on new
domains, especially when going from synthetic to real data. In this paper we
address the problem of unsupervised domain adaptation (UDA), which attempts to
train on labelled data from one domain (source domain), and simultaneously
learn from unlabelled data in the domain of interest (target domain). Existing
methods have seen success by training on pseudo-labels for these unlabelled
images. Multiple techniques have been proposed to mitigate low-quality
pseudo-labels arising from the domain shift, with varying degrees of success.
We propose DACS: Domain Adaptation via Cross-domain mixed Sampling, which mixes
images from the two domains along with the corresponding labels and
pseudo-labels. These mixed samples are then trained on, in addition to the
labelled data itself. We demonstrate the effectiveness of our solution by
achieving state-of-the-art results for GTA5 to Cityscapes, a common
synthetic-to-real semantic segmentation benchmark for UDA.
