When one models the considered sets of samples as empirical probability distributions, Optimal Transport (OT)frameworkprovides asolution tofind,without supervision, asoft-correspondence mapbetweenthemgivenbyan optimalcoupling.

Recent approaches include priors on the feature attribution of a deep neural network (DNN) into the training process to reduce the dependence on unwanted features. However, until now one needed to trade off high-quality attributions, satisfying desirable axioms, against the time required to compute them. This in turn either led to long training times or ineffective attribution priors.