  Semantic segmentation is a key problem for many computer vision tasks. While
approaches based on convolutional neural networks constantly break new records
on different benchmarks, generalizing well to diverse testing environments
remains a major challenge. In numerous real world applications, there is indeed
a large gap between data distributions in train and test domains, which results
in severe performance loss at run-time. In this work, we address the task of
unsupervised domain adaptation in semantic segmentation with losses based on
the entropy of the pixel-wise predictions. To this end, we propose two novel,
complementary methods using (i) entropy loss and (ii) adversarial loss
respectively. We demonstrate state-of-the-art performance in semantic
segmentation on two challenging "synthetic-2-real" set-ups and show that the
approach can also be used for detection.
