  Training a semantic segmentation model requires a large amount of pixel-level
annotation, hampering its application at scale. With computer graphics, we can
generate almost unlimited training data with precise annotation. However,a deep
model trained with synthetic data usually cannot directly generalize well to
realistic images due to domain shift. It has been observed that highly
confident labels for the unlabeled real images may be predicted relying on the
labeled synthetic data. To tackle the unsupervised domain adaptation problem,
we explore the possibilities to generate high-quality labels as proxy labels to
supervise the training on target data. Specifically, we propose a novel
proxy-based method using multi-adversarial training. We first train the model
using synthetic data (source domain). Multiple discriminators are used to align
the features be-tween the source and target domain (real images) at different
levels. Then we focus on obtaining and selecting high-quality proxy labels by
incorporating both the confidence of the class predictor and that from the
adversarial discriminators. Our discriminators not only work as a regularizer
to encourage feature alignment but also provide an alternative confidence
measure for generating proxy labels. Relying on the generated high-quality
proxies, our model can be trained in a "supervised manner" on the target
do-main. On two major tasks, GTA5->Cityscapes and SYNTHIA->Cityscapes, our
method achieves state-of-the-art results, outperforming the previous by a large
margin.
