  Unsupervised Domain Adaptation (UDA) essentially trades a model's performance
on a source domain for improving its performance on a target domain. To resolve
the issue, Unsupervised Domain Expansion (UDE) has been proposed recently. UDE
tries to adapt the model for the target domain as UDA does, and in the meantime
maintains its source-domain performance. In both UDA and UDE settings, a model
tailored to a given domain, let it be the source or the target domain, is
assumed to well handle samples from the given domain. We question the
assumption by reporting the existence of cross-domain visual ambiguity: Given
the lack of a crystally clear boundary between the two domains, samples from
one domain can be visually close to the other domain. Such sorts of samples are
typically in minority in their host domain, so they tend to be overlooked by
the domain-specific model, but can be better handled by a model from the other
domain. We exploit this finding, and accordingly propose Co-Teaching (CT). The
CT method is instantiated with knowledge distillation based CT (kdCT) plus
mixup based CT (miCT). Specifically, kdCT transfers knowledge from a
leading-teacher network and an assistant-teacher network to a student network,
so the cross-domain ambiguity will be better handled by the student. Meanwhile,
miCT further enhances the generalization ability of the student. Extensive
experiments on two image classification datasets and two driving-scene
segmentation datasets justify the viability of CT for UDA and UDE.
