Rethinking Joint Maximum Mean Discrepancy for Visual Domain Adaptation

TL;DR

In domain adaption (DA), joint maximum mean discrepancy (JMMD), as a famous distribution-distance metric, aims to measure joint probability distribution difference between the source domain and target domain, while it is still not fully explored and especially hard to be applied into a subspace-learning framework as its empirical estimation involve…

Opening excerpt from the authors’ abstract. source