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 all 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 preserves the core axiomatic properties of IG in a generalized weighted-baseline form. Under an expected, proxy-based fitness--relevance monotonicity assumption, WG provides a probabilistic justification for assigning larger weights to more informative baselines. Experiments on commonly used image datasets and models show that WG improves over EG under our protocol, with up to 36% gains across evaluated convolutional and Transformer architectures. These gains come with additional fitness-evaluation cost, so WG should be viewed as an attribution-fidelity trade-off rather than a faster alternative to EG. By moving beyond the assumption that all baselines contribute equally, Weighted Integrated Gradients offers a clearer and more reliable approach to explaining computer-vision models, improving both understanding and practical usability in explainable AI.

翻译：积分梯度（IG）是可解释人工智能中广泛使用的归因方法，尤其在需要可靠特征归因的计算机视觉应用中。IG的一个关键局限性在于其对基线（参考）图像选择的敏感性。多基线扩展方法如期望梯度（EG）假设基线权重均匀分布，隐含地将所有基线图像视为同等信息。在高维视觉模型中，这种假设常导致解释结果噪声大或不稳定。本文提出加权积分梯度（WG），这是一种通过评估并加权基线来增强归因可靠性的原理性方法。WG引入无监督的基线适用性标准，实现对每个输入的自适应基线选择与加权。该方法以广义加权基线形式保留了IG的核心公理性质。基于预期代理适配性-相关性单调性假设，WG为更优基线赋予更大权重提供了概率依据。在常用图像数据集和模型上的实验表明，WG在评估协议下优于EG，在卷积和Transformer架构中最高可获得36%的性能提升。这些提升伴随额外的适配性评估成本，因此WG应被视为归因保真度与计算开销之间的权衡方案，而非EG的加速替代。通过突破所有基线贡献均等的假设，加权积分梯度为解释计算机视觉模型提供了更清晰可靠的方法，提升了可解释人工智能的理解深度与实践可用性。