  This work explores the novel idea of learning a submodular scoring function
to improve the specificity/selectivity of existing feature attribution methods.
Submodular scores are natural for attribution as they are known to accurately
model the principle of diminishing returns. A new formulation for learning a
deep submodular set function that is consistent with the real-valued
attribution maps obtained by existing attribution methods is proposed. The
final attribution value of a feature is then defined as the marginal gain in
the induced submodular score of the feature in the context of other highly
attributed features, thus decreasing the attribution of redundant yet
discriminatory features. Experiments on multiple datasets illustrate that the
proposed attribution method achieves higher specificity along with good
discriminative power. The implementation of our method is publicly available at
https://github.com/Piyushi-0/SEA-NN.
