  Eliminating the covariate shift cross domains is one of the common methods to
deal with the issue of domain shift in visual unsupervised domain adaptation.
However, current alignment methods, especially the prototype based or
sample-level based methods neglect the structural properties of the underlying
distribution and even break the condition of covariate shift. To relieve the
limitations and conflicts, we introduce a novel concept named (virtual) mirror,
which represents the equivalent sample in another domain. The equivalent sample
pairs, named mirror pairs reflect the natural correspondence of the empirical
distributions. Then a mirror loss, which aligns the mirror pairs cross domains,
is constructed to enhance the alignment of the domains. The proposed method
does not distort the internal structure of the underlying distribution. We also
provide theoretical proof that the mirror samples and mirror loss have better
asymptotic properties in reducing the domain shift. By applying the virtual
mirror and mirror loss to the generic unsupervised domain adaptation model, we
achieved consistent superior performance on several mainstream benchmarks. Code
is available at https://github.com/CTI-VISION/Mirror-Sample
