R-Cut:在视觉转换器中增强可解释性,以关系权衡和切割.
Yingjie Niu1, Ming Ding1, Maoning Ge1
1Graduate School of Informatics, Nagoya University, Nagoya 464-8603, Japan.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
这项研究引入了一种新方法,使基于变压器的图像分类模型更容易解释. 可视化特定类别的地图增强了对计算机视觉中的AI决策的信任和理解.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习可解释性 机器学习可解释性
背景情况:
- 变压器模型越来越多地用于自然语言处理 (NLP),计算机视觉和多模式应用,如GPT4.4.
- 提高这些强大的模型的可解释性对于建立信任和使下游任务更深入地理解至关重要.
- 当前的方法往往缺乏确定特定特征对图像分类的贡献所需的细分度.
研究的目的:
- 开发一种新的方法来提高基于变压器的图像分类模型的可解释性.
- 为用户提供类特定地图的可视化,以便更好地理解模型预测.
- 增加对人工智能驱动图像分类结果的信任.
主要方法:
- 引入了两个新型模块:"关系加权"用于提取特定类别的中间特征和"切割"用于细粒度特征分解 (位置,纹理,颜色).
- 集成这些模块生成密集的,类特定的视觉可解释性地图.
- 通过在ImageNet和LRN数据集 (自动驾驶危险警报) 上进行广泛的定性和定量实验来验证该方法.
主要成果:
- 与以前的标准和复杂数据集方法相比,拟议的方法显著提高了可解释性.
- 废弃实验证实了"关系权衡"和"切割"模块的有效性和个体贡献.
- 生成了详细的视觉地图,清楚地突出了特定类的相关特征.
结论:
- 这种新方法有效地提高了基于变压器的图像分类器的可解释性.
- 生成的类特定地图为模型决策过程提供了有价值的见解.
- 这种方法提供了一个强大的解决方案,可以增加对人工智能支持的图像分析的信任和理解,特别是在安全关键的应用中.
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