几何移除再训练（GOAR）：坐标不变的XAI评估
Geometric Remove-and-Retrain (GOAR): Coordinate-Invariant eXplainable AI Assessment
AI总结：
本文提出坐标不变的几何移除再训练（GOAR）方法，以克服像素扰动指标在XAI评估中无法区分特征归因方法的局限性，并通过合成与真实数据集实验验证其有效性。
摘要：
识别对输出结果具有关键影响的输入特征对于可解释人工智能（XAI）的发展不可或缺。移除再训练（ROAR）是一种被广泛接受的评估单个像素重要性的方法，它通过测量移除像素后修改数据集的再训练精度变化来评估。然而，我们发现了像素扰动策略中的显著局限性。从几何视角来看，我们发现这些指标无法区分不同特征归因方法之间的差异，从而损害了评估的可靠性。为解决这一挑战，我们提出了一种替代性的特征扰动方法，名为几何移除再训练（GOAR）。通过一系列使用合成和真实数据集的实验，我们证实GOAR超越了基于像素的指标的局限性。
英文摘要：
Identifying the relevant input features that have a critical influence on the output results is indispensable for the development of explainable artificial intelligence (XAI). Remove-and-Retrain (ROAR) is a widely accepted approach for assessing the importance of individual pixels by measuring changes in accuracy following their removal and subsequent retraining of the modified dataset. However, we uncover notable limitations in pixel-perturbation strategies. When viewed from a geometric perspective, we discover that these metrics fail to discriminate between differences among feature attribution methods, thereby compromising the reliability of the evaluation. To address this challenge, we introduce an alternative feature-perturbation approach named Geometric Remove-and-Retrain (GOAR). Through a series of experiments with both synthetic and real datasets, we substantiate that GOAR transcends the limitations of pixel-centric metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
