  Unsupervised domain adaptation (UDA) aims to transfer the knowledge from the
labeled source domain to the unlabeled target domain. Existing self-training
based UDA approaches assign pseudo labels for target data and treat them as
ground truth labels to fully leverage unlabeled target data for model
adaptation. However, the generated pseudo labels from the model optimized on
the source domain inevitably contain noise due to the domain gap. To tackle
this issue, we advance a MetaCorrection framework, where a Domain-aware
Meta-learning strategy is devised to benefit Loss Correction (DMLC) for UDA
semantic segmentation. In particular, we model the noise distribution of pseudo
labels in target domain by introducing a noise transition matrix (NTM) and
construct meta data set with domain-invariant source data to guide the
estimation of NTM. Through the risk minimization on the meta data set, the
optimized NTM thus can correct the noisy issues in pseudo labels and enhance
the generalization ability of the model on the target data. Considering the
capacity gap between shallow and deep features, we further employ the proposed
DMLC strategy to provide matched and compatible supervision signals for
different level features, thereby ensuring deep adaptation. Extensive
experimental results highlight the effectiveness of our method against existing
state-of-the-art methods on three benchmarks.
