  In computer vision, data shift has proven to be a major barrier for safe and
robust deep learning applications. In medical applications, histopathological
images are often associated with data shift and they are hardly available. It
is important to understand to what extent a model can be made robust against
data shift using all available data. Here, we first show that domain
adversarial methods can be very deleterious if they are wrongly used. We then
use domain adversarial methods to transfer data shift invariance from one
dataset to another dataset with different semantics and show that domain
adversarial methods are efficient inter-semantically with similar performance
than intra-semantical domain adversarial methods.
