  The world we see is ever-changing and it always changes with people, things,
and the environment. Domain is referred to as the state of the world at a
certain moment. A research problem is characterized as transfer adaptation
learning (TAL) when it needs knowledge correspondence between different
moments/domains. Conventional machine learning aims to find a model with the
minimum expected risk on test data by minimizing the regularized empirical risk
on the training data, which, however, supposes that the training and test data
share similar joint probability distribution. TAL aims to build models that can
perform tasks of target domain by learning knowledge from a semantic related
but distribution different source domain. It is an energetic research filed of
increasing influence and importance, which is presenting a blowout publication
trend. This paper surveys the advances of TAL methodologies in the past decade,
and the technical challenges and essential problems of TAL have been observed
and discussed with deep insights and new perspectives. Broader solutions of
transfer adaptation learning being created by researchers are identified, i.e.,
instance re-weighting adaptation, feature adaptation, classifier adaptation,
deep network adaptation and adversarial adaptation, which are beyond the early
semi-supervised and unsupervised split. The survey helps researchers rapidly
but comprehensively understand and identify the research foundation, research
status, theoretical limitations, future challenges and under-studied issues
(universality, interpretability, and credibility) to be broken in the field
toward universal representation and safe applications in open-world scenarios.
