  Open compound domain adaptation (OCDA) aims to transfer knowledge from a
labeled source domain to a mix of unlabeled homogeneous compound target domains
while generalizing to open unseen domains. Existing OCDA methods solve the
intra-domain gaps by a divide-and-conquer strategy, which divides the problem
into several individual and parallel domain adaptation (DA) tasks. Such
approaches often contain multiple sub-networks or stages, which may constrain
the model's performance. In this work, starting from the general DA theory, we
establish the generalization bound for the setting of OCDA. Built upon this, we
argue that conventional OCDA approaches may substantially underestimate the
inherent variance inside the compound target domains for model generalization.
We subsequently present Stochastic Compound Mixing (SCMix), an augmentation
strategy with the primary objective of mitigating the divergence between source
and mixed target distributions. We provide theoretical analysis to substantiate
the superiority of SCMix and prove that the previous methods are sub-groups of
our methods. Extensive experiments show that our method attains a lower
empirical risk on OCDA semantic segmentation tasks, thus supporting our
theories. Combining the transformer architecture, SCMix achieves a notable
performance boost compared to the SoTA results.
