DEPICT:在图像分类任务中扩散启用变换的重要性
Sarah Jabbour1, Gregory Kondas1, Ella Kazerooni1
1University of Michigan, Ann Arbor, MI, USA.
概括
我们引入了一种新的方法来理解图像分类器,通过 permuting 概念,而不是像素. 这种方法揭示了特定概念对模型决策的重要性,提供了超越个人图像分析的全球洞察力.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 目前的图像模型解释方法,如激活地图,主要提供基于像素空间内的基于实例的洞察力.
- 这种以像素为中心的方法限制了对全球图像分类器行为和特征重要性的理解.
- 基于变的方法对于表格数据是有效的,但缺乏对图像模型的直接应用.
研究的目的:
- 为图像分类器开发一种新的基于换的解释方法.
- 通过评估概念的重要性,使全球模型行为更全面地理解.
- 为图像分类模型提供特征重要性排名.
主要方法:
- 提出一种方法,在数据集中将可解释的概念 (例如,来自图像标题) 进行变换.
- 使用文本受条件的扩散模型来生成具有置概念的新图像.
- 通过评估分类器性能变化对变换与未变换数据的变化来衡量特征的重要性.
主要成果:
- 提出的方法成功地恢复了基本模型特征的重要性.
- 在合成和现实世界图像分类任务中表现出有效性.
- 为图像分类器生成概念重要性排名列表.
结论:
- 基于变换的概念操纵为图像模型的基于像素的解释提供了一个强大的替代方案.
- 这种方法通过评估语义概念对分类的影响来提高解释性.
- 该方法为图像分类器如何利用特征提供了有价值的全球见解.
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