  Deep learning usually achieves the best results with complete supervision. In
the case of semantic segmentation, this means that large amounts of pixelwise
annotations are required to learn accurate models. In this paper, we show that
we can obtain state-of-the-art results using a semi-supervised approach,
specifically a self-training paradigm. We first train a teacher model on
labeled data, and then generate pseudo labels on a large set of unlabeled data.
Our robust training framework can digest human-annotated and pseudo labels
jointly and achieve top performances on Cityscapes, CamVid and KITTI datasets
while requiring significantly less supervision. We also demonstrate the
effectiveness of self-training on a challenging cross-domain generalization
task, outperforming conventional finetuning method by a large margin. Lastly,
to alleviate the computational burden caused by the large amount of pseudo
labels, we propose a fast training schedule to accelerate the training of
segmentation models by up to 2x without performance degradation.
