  Deep learning-based semantic segmentation methods have an intrinsic
limitation that training a model requires a large amount of data with
pixel-level annotations. To address this challenging issue, many researchers
give attention to unsupervised domain adaptation for semantic segmentation.
Unsupervised domain adaptation seeks to adapt the model trained on the source
domain to the target domain. In this paper, we introduce a self-ensembling
technique, one of the successful methods for domain adaptation in
classification. However, applying self-ensembling to semantic segmentation is
very difficult because heavily-tuned manual data augmentation used in
self-ensembling is not useful to reduce the large domain gap in the semantic
segmentation. To overcome this limitation, we propose a novel framework
consisting of two components, which are complementary to each other. First, we
present a data augmentation method based on Generative Adversarial Networks
(GANs), which is computationally efficient and effective to facilitate domain
alignment. Given those augmented images, we apply self-ensembling to enhance
the performance of the segmentation network on the target domain. The proposed
method outperforms state-of-the-art semantic segmentation methods on
unsupervised domain adaptation benchmarks.
