  Semantic segmentation is an important sub-task for many applications, but
pixel-level ground truth labeling is costly and there is a tendency to overfit
the training data, limiting generalization. Unsupervised domain adaptation can
potentially address these problems, allowing systems trained on labelled
datasets from one or more source domains (including less expensive synthetic
domains) to be adapted to novel target domains. The conventional approach is to
automatically align the representational distributions of source and target
domains. One limitation of this approach is that it tends to disadvantage lower
probability classes. We address this problem by introducing a Class-Conditional
Domain Adaptation method (CCDA). It includes a class-conditional multi-scale
discriminator and the class-conditional loss. This novel CCDA method encourages
the network to shift the domain in a class-conditional manner, and it equalizes
loss over classes. We evaluate our CCDA method on two transfer tasks and
demonstrate performance comparable to state-of-the-art methods.
