  Open compound domain adaptation (OCDA) is a domain adaptation setting, where
target domain is modeled as a compound of multiple unknown homogeneous domains,
which brings the advantage of improved generalization to unseen domains. In
this work, we propose a principled meta-learning based approach to OCDA for
semantic segmentation, MOCDA, by modeling the unlabeled target domain
continuously. Our approach consists of four key steps. First, we cluster target
domain into multiple sub-target domains by image styles, extracted in an
unsupervised manner. Then, different sub-target domains are split into
independent branches, for which batch normalization parameters are learnt to
treat them independently. A meta-learner is thereafter deployed to learn to
fuse sub-target domain-specific predictions, conditioned upon the style code.
Meanwhile, we learn to online update the model by model-agnostic meta-learning
(MAML) algorithm, thus to further improve generalization. We validate the
benefits of our approach by extensive experiments on synthetic-to-real
knowledge transfer benchmark datasets, where we achieve the state-of-the-art
performance in both compound and open domains.
