一个模特真正看的是什么? : 提取以模型为导向的概念来解释深度神经网络.
IEEE transactions on pattern analysis and machine intelligence
|January 23, 2024
概括
本研究介绍了以模型为导向的概念提取 (MOCE) 对于AI可解释性. MOCE直接从图像分类模型中发现概念,提供了一个独特的视角,不受人类或细分偏见的过.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 模型可解释性对于可信的人工智能至关重要,特别是在自动驾驶和医学诊断等关键应用中.
- 基于概念的解释旨在视觉解释预先训练的图像分类模型,例如卷积神经网络.
- 现有的方法通常依赖于人类定义的概念或细分,可能误解模型的内部视角.
研究的目的:
- 开发一种新的方法,从图像分类模型中提取以模型为中心的概念.
- 克服人类定义或基于细分的概念提取方法的局限性.
- 确保解释准确地反映了模型独特的学习观点.
主要方法:
- 提出面向模型的概念提取 (MOCE),一种仅基于人工智能模型的内部运作来识别概念的方法.
- 专注于发现模型内在学习的概念,独立于外部的人类定义或细分算法.
主要成果:
- 对各种预训练模型的实验验证证明了MOCE的有效性.
- 结果证实,MOCE成功地提取了真正代表模型观点的概念.
- 与以前的方法相比,MOCE提供了一个更真实的以模型为中心的解释.
结论:
- 面向模型的概念提取 (MOCE) 在AI解释性方面取得了重大进展.
- 通过专注于模型,MOCE更准确地捕捉了其独特的视角.
- 这种方法通过提供对其决策过程的真实见解,提高了人工智能系统的可信度.
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