The new machine learning techniques of transfer learning and domain adaptation have recently captured special attention in the computer vision community.  In this talk we will take a look at some of the methods that have been recently adopted or developed for adaptation of learning in the visual domains.  We will also try to have an open discussion over some of more ideological questions such as better generalization versus adaptation. With abundance of massive volumes of visual training data should we keep at designing algorithms that could model all the possible variations in the visual world or should we regard adaptation as an integral part of learning in the visual domains?