  We propose a new approach, called self-motivated pyramid curriculum domain
adaptation (PyCDA), to facilitate the adaptation of semantic segmentation
neural networks from synthetic source domains to real target domains. Our
approach draws on an insight connecting two existing works: curriculum domain
adaptation and self-training. Inspired by the former, PyCDA constructs a
pyramid curriculum which contains various properties about the target domain.
Those properties are mainly about the desired label distributions over the
target domain images, image regions, and pixels. By enforcing the segmentation
neural network to observe those properties, we can improve the network's
generalization capability to the target domain. Motivated by the self-training,
we infer this pyramid of properties by resorting to the semantic segmentation
network itself. Unlike prior work, we do not need to maintain any additional
models (e.g., logistic regression or discriminator networks) or to solve minmax
problems which are often difficult to optimize. We report state-of-the-art
results for the adaptation from both GTAV and SYNTHIA to Cityscapes, two
popular settings in unsupervised domain adaptation for semantic segmentation.
