混合样本数据增强对神经网络可解释性的影响
Soyoun Won1, Sung-Ho Bae1, Seong Tae Kim1
1Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, 17104, Gyeonggi-do, South Korea.
概括
混合样本数据增强可以降低深度神经网络 (DNN) 的解释性,特别是当标签混合时. 对于依赖特征属性图的应用程序来说,仔细采用是至关重要的.
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:
- 混合样本数据增强在训练深度神经网络 (DNN) 中很常见.
- 虽然它对各种任务有效,但它对模型解释性的影响仍未得到充分研究.
- 特性归属地图是理解DNN决策的关键.
研究的目的:
- 调查混合样本数据增强和模型可解释性之间的关系.
- 具体分析其对特征属性图的影响.
- 引入一种新的度量来比较可解释性,同时控制阻塞强度.
主要方法:
- 混合样本数据增强技术的探索.
- 开发一种新的指标来量化模型的可解释性.
- 使用新指标进行不同增强策略的实验比较.
主要成果:
- 发现一些混合样本数据增强方法降低了模型的解释性.
- 在增强过程中标签混合显著导致解释性降低.
- 拟议的指标有效地隔离了对可解释性的影响.
结论:
- 混合样本数据增强可能会对DNN的解释性产生负面影响,特别是在特征归属图上.
- 标签混合是使用这些增强技术时需要考虑的关键因素.
- 研究人员和从业人员应仔细选择可解释性敏感应用中的增强策略.
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