  For unsupervised domain adaptation problems, the strategy of aligning the two
domains in latent feature space through adversarial learning has achieved much
progress in image classification, but usually fails in semantic segmentation
tasks in which the latent representations are overcomplex. In this work, we
equip the adversarial network with a "significance-aware information bottleneck
(SIB)", to address the above problem. The new network structure, called SIBAN,
enables a significance-aware feature purification before the adversarial
adaptation, which eases the feature alignment and stabilizes the adversarial
training course. In two domain adaptation tasks, i.e., GTA5 -> Cityscapes and
SYNTHIA -> Cityscapes, we validate that the proposed method can yield leading
results compared with other feature-space alternatives. Moreover, SIBAN can
even match the state-of-the-art output-space methods in segmentation accuracy,
while the latter are often considered to be better choices for domain adaptive
segmentation task.
