  In computer vision, one is often confronted with problems of domain shifts,
which occur when one applies a classifier trained on a source dataset to target
data sharing similar characteristics (e.g. same classes), but also different
latent data structures (e.g. different acquisition conditions). In such a
situation, the model will perform poorly on the new data, since the classifier
is specialized to recognize visual cues specific to the source domain. In this
work we explore a solution, named DeepJDOT, to tackle this problem: through a
measure of discrepancy on joint deep representations/labels based on optimal
transport, we not only learn new data representations aligned between the
source and target domain, but also simultaneously preserve the discriminative
information used by the classifier. We applied DeepJDOT to a series of visual
recognition tasks, where it compares favorably against state-of-the-art deep
domain adaptation methods.
