Enhancing Visual Feature Attribution via Weighted Integrated Gradients

TL;DR

Weighted Integrated Gradients (WG) improves feature attribution in computer vision by adaptively weighting baselines, leading to more reliable explanations and better fidelity than traditional methods like Expected Gradients.

Contribution

This paper introduces WG, an unsupervised, adaptive baseline weighting method for Integrated Gradients, enhancing explanation stability and fidelity in high-dimensional vision models.

Findings

WG outperforms Expected Gradients with 10-35% fidelity improvements.

WG identifies informative baseline subsets, reducing explanation variability.

The method maintains core axioms of IG while providing theoretical guarantees.

Abstract

Integrated Gradients (IG) is a widely used attribution method in explainable AI, particularly in computer vision applications where reliable feature attribution is essential. A key limitation of IG is its sensitivity to the choice of baseline (reference) images. Multi-baseline extensions such as Expected Gradients (EG) assume uniform weighting over baselines, implicitly treating baseline images as equally informative. In high-dimensional vision models, this assumption often leads to noisy or unstable explanations. This paper proposes Weighted Integrated Gradients (WG), a principled approach that evaluates and weights baselines to enhance attribution reliability. WG introduces an unsupervised criterion for baseline suitability, enabling adaptive selection and weighting of baselines on a per-input basis. The method not only preserves core axiomatic properties of IG but also provides improved theoretical guarantees on the quality of explanation over EG. Experiments on commonly used image datasets and models show that WG consistently outperforms EG, yielding 10 to 35 percent improvements in attribution fidelity. WG further identifies informative baseline subsets, reducing unnecessary variability while maintaining high attribution accuracy. By moving beyond the idea that all baselines matter equally, Weighted Integrated Gradients offers a clearer and more reliable way to explain computer-vision models, improving both understanding and practical usability in explainable AI.