Unsupervised domain adaptation without consuming annotation process for
unlabeled target data attracts appealing interests in semantic segmentation.
However, 1) existing methods neglect that not all semantic representations
across domains are transferable, which cripples domain-wise transfer with
untransferable knowledge; 2) they fail to narrow category-wise distribution
shift due to category-agnostic feature alignment. To address above challenges,
we develop a new Critical Semantic-Consistent Learning (CSCL) model, which
mitigates the discrepancy of both domain-wise and category-wise distributions.
Specifically, a critical transfer based adversarial framework is designed to
highlight transferable domain-wise knowledge while neglecting untransferable
knowledge. Transferability-critic guides transferability-quantizer to maximize
positive transfer gain under reinforcement learning manner, although negative
transfer of untransferable knowledge occurs. Meanwhile, with the help of
confidence-guided pseudo labels generator of target samples, a symmetric soft
divergence loss is presented to explore inter-class relationships and
facilitate category-wise distribution alignment. Experiments on several
datasets demonstrate the superiority of our model.

本文提出了一种基于转移学习和关键语义一致性的模型，是一种无监督域自适应的方法来减少领域内、类别内的分布差异，在语义分割上具有优越性。