A Feature Importance Explanation Methods

We briefly review several FI explanation methods and explain how they are used in this paper. This method follows SHAP exactly except for the use of a regression. To estimate a feature's importance, we aim to compute the expected difference between model For the full sequential tuning process across all hyperparameters, see Appendix F. We consider five different The Shuffle function shuffles elements of the input representation across all bounding boxes that need replacement within one sample (within and across bounding boxes). The resulting explanation is differentiable w.r.t. Below, we describe how the compute budget can vary for each method.