  Collection of real world annotations for training semantic segmentation
models is an expensive process. Unsupervised domain adaptation (UDA) tries to
solve this problem by studying how more accessible data such as synthetic data
can be used to train and adapt models to real world images without requiring
their annotations. Recent UDA methods applies self-learning by training on
pixel-wise classification loss using a student and teacher network. In this
paper, we propose the addition of a consistency regularization term to
semi-supervised UDA by modelling the inter-pixel relationship between elements
in networks' output. We demonstrate the effectiveness of the proposed
consistency regularization term by applying it to the state-of-the-art DAFormer
framework and improving mIoU19 performance on the GTA5 to Cityscapes benchmark
by 0.8 and mIou16 performance on the SYNTHIA to Cityscapes benchmark by 1.2.
