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.

在无监督的领域自适应问题中，我们引入了 “意义感知信息瓶颈（SIB）” 的对抗网络，从而形成了新的网络结构 SIBAN 来解决现有的特征对齐问题，并在两个领域适应任务中取得了优异的结果。