🤖 AI Summary
Existing path-based feature attribution methods define trajectories in the input space, rendering them susceptible to path artifacts and unable to discern the semantic significance of input perturbations, which leads to unstable explanations. This work proposes Reveal-IG, a novel framework that lifts path attribution from the input space into a structured probe distribution space centered around the target sample, computing integrated gradients along distributional paths with respect to the model’s expected output. By supporting multi-scale image probes and modeling feature uncertainty in tabular data, Reveal-IG preserves attribution completeness while avoiding input-space artifacts. Experiments demonstrate that Reveal-IG produces stable, signed attributions on ImageNet classification and tabular regression tasks, significantly outperforming existing methods on sign-dependent metrics and remaining competitive on others, with synthetic diagnostics further confirming its robustness against artifacts.
📝 Abstract
Feature attribution methods explain predictions by assigning importance scores to input features. Path-based methods such as Integrated Gradients are especially appealing because they satisfy \textit{completeness}: attributions sum to the change in model output between a reference state and the input. Yet most path methods define this trajectory in input space, explaining a model through pointwise perturbed inputs along a chosen path. An input-space path integrates the model's raw response at each point it passes through, with no control over the resolution at which a feature is queried; the early, baseline-adjacent part of the trajectory contributes to the explanation on equal footing with the input itself. Here, we lift path attribution from input space to a space of structured probe distributions around the example of interest, and call our method Reveal-IG. Rather than traversing raw input values, Reveal-IG progressively reveals information about the input and attributes changes in the model's expected output along this distributional path. The result is a path-attribution framework that retains completeness with respect to the expected model response, and naturally accommodates multiscale image probes and feature-wise uncertainty in tabular data. Synthetic diagnostics show that Reveal-IG avoids path artifacts that affect input-space methods, and across ImageNet classification and tabular regression it produces stable, signed attributions -- leading on metrics that use attribution sign while remaining competitive on the rest.