Title: Improving Explanation Reliability through Group Attribution. Abstract: Although input attribution methods are mainstream in understanding predictions of DNNs for straightforward interpretations, the non-linearity of DNNs often makes the attributed scores unreliable in explaining a given prediction, deteriorating the faithfulness of the explanation. 
However, the challenge could be mitigated by attributing scores to groups of explanatory components instead of the individuals, termed group attribution. While a group attribution would explain the group-wise contribution more reliably, it does not explain the component-wise contributions so that estimating component-wise scores yields less reliable explanation, indicating the trade-off of group attributions.
In this work, we introduce the generalized definition of reliability loss and group attribution, and formulate the optimization problem of the trade-off with these terms. We apply our formalization to Shapley value attribution to propose the optimization method G-SHAP. We show the effectiveness and explanatory benefits of our method through empirical results on image classification tasks.