  The divergence between labeled training data and unlabeled testing data is a
significant challenge for recent deep learning models. Unsupervised domain
adaptation (UDA) attempts to solve such a problem. Recent works show that
self-training is a powerful approach to UDA. However, existing methods have
difficulty in balancing scalability and performance. In this paper, we propose
an instance adaptive self-training framework for UDA on the task of semantic
segmentation. To effectively improve the quality of pseudo-labels, we develop a
novel pseudo-label generation strategy with an instance adaptive selector.
Besides, we propose the region-guided regularization to smooth the pseudo-label
region and sharpen the non-pseudo-label region. Our method is so concise and
efficient that it is easy to be generalized to other unsupervised domain
adaptation methods. Experiments on 'GTA5 to Cityscapes' and 'SYNTHIA to
Cityscapes' demonstrate the superior performance of our approach compared with
the state-of-the-art methods.
