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. This formulation not only ensures that the scores for the heat maps that include the highly attributed features across the existing methods are high, but also that the score saturates even for the most specific heat map. 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 while not degrading the discriminative power.

翻译：这项工作探索了学习子模块评分功能的新理念,以改善现有特征归属方法的特性/选择性。亚模块评分在归属方面是自然的,因为已知分数准确地模拟了收益减少原则。提出了与现有属性归属方法获得的实际价值归属图相一致的深子模块组合函数的新提法。这一提法不仅确保热图的评分包括现有方法中高度归属特征,而且确保得分饱和度,即使是最具体的热图也是如此。然后,一个特征的最后归分值被定义为在其他高度属性特征背景下该特征诱导子模块评分的边际收益,从而减少冗余但歧视性特征的归属。多数据集实验表明,拟议的归分方法在不贬低歧视力量的情况下具有更高的特性。