  Many research efforts have been committed to unsupervised domain adaptation
(DA) problems that transfer knowledge learned from a labeled source domain to
an unlabeled target domain. Various DA methods have achieved remarkable results
recently in terms of predicting ability, which implies the effectiveness of the
aforementioned knowledge transferring. However, state-of-the-art methods rarely
probe deeper into the transferred mechanism, leaving the true essence of such
knowledge obscure. Recognizing its importance in the adaptation process, we
propose an interpretive model of unsupervised domain adaptation, as the first
attempt to visually unveil the mystery of transferred knowledge. Adapting the
existing concept of the prototype from visual image interpretation to the DA
task, our model similarly extracts shared information from the domain-invariant
representations as prototype vectors. Furthermore, we extend the current
prototype method with our novel prediction calibration and knowledge fidelity
preservation modules, to orientate the learned prototypes to the actual
transferred knowledge. By visualizing these prototypes, our method not only
provides an intuitive explanation for the base model's predictions but also
unveils transfer knowledge by matching the image patches with the same
semantics across both source and target domains. Comprehensive experiments and
in-depth explorations demonstrate the efficacy of our method in understanding
the transferred mechanism and its potential in downstream tasks including model
diagnosis.
