使LIME焕发生机：用于LIME分析的视觉交互测试平台
Vivifying LIME: Visual Interactive Testbed for LIME Analysis
AI总结：
本文提出LIMEVis，一种交互式可视化工具，用于提升LIME分析的效率，通过同时探索多个结果并直接修改以识别模型分类的关键特征。
摘要：
可解释人工智能（XAI）在解释模型预测中变得尤为重要。在领先的XAI技术中，局部可解释模型无关解释（LIME）最常被使用，因为它显著帮助人们理解复杂模型。然而，LIME的分析受到每次仅限于单张图像的限制。此外，它缺乏观察LIME结果和直接操纵影响结果因素的交互机制。为了解决这些问题，我们引入了一个交互式可视化工具，LIMEVis，它通过允许用户同时探索多个LIME结果并直接修改它们来改进LIME的分析流程。借助LIMEVis，我们可以方便地识别图像中模型似乎主要考虑用于类别分类的共同特征。此外，通过交互式修改LIME结果，我们可以确定图像中哪些部分影响模型的分类。
英文摘要：
Explainable Artificial Intelligence (XAI) has gained importance in interpreting model predictions. Among leading techniques for XAI, Local Interpretable Model-agnostic Explanations (LIME) is most frequently utilized as it notably helps people's understanding of complex models. However, LIME's analysis is constrained to a single image at a time. Besides, it lacks interaction mechanisms for observing the LIME's results and direct manipulations of factors affecting the results. To address these issues, we introduce an interactive visualization tool, LIMEVis, which improves the analysis workflow of LIME by enabling users to explore multiple LIME results simultaneously and modify them directly. With LIMEVis, we could conveniently identify common features in images that a model seems to mainly consider for category classification. Additionally, by interactively modifying the LIME results, we could determine which segments in an image influence the model's classification.
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。
