LearningtoAdaptviaLatentDomainsforAdaptive SemanticSegmentation

Semantic segmentation is a popular task in computer vision, which assigns pixel-wise semantic labels for given images. It has been widely utilized to facilitate downstream applications such as video surveillance and autonomous driving. Recent progress on image semantic segmentation has been drivenbydeep neural networks trained onalargeamount oflabeled data, which are yet expensive to obtain. An alternative way is to generate synthetic images with pixel-level ground truth readily available in an effortless way [1,2].