  Pseudo-labelling is a popular technique in unsuper-vised domain adaptation
for semantic segmentation. However, pseudo labels are noisy and inevitably have
confirmation bias due to the discrepancy between source and target domains and
training process. In this paper, we train the model by the pseudo labels which
are implicitly produced by itself to learn new complementary knowledge about
target domain. Specifically, we propose a tri-learning architecture, where
every two branches produce the pseudo labels to train the third one. And we
align the pseudo labels based on the similarity of the probability
distributions for each two branches. To further implicitly utilize the pseudo
labels, we maximize the distances of features for different classes and
minimize the distances for the same classes by triplet loss. Extensive
experiments on GTA5 to Cityscapes and SYNTHIA to Cityscapes tasks show that the
proposed method has considerable improvements.
