可解释的神经网络:原则和应用
Zhuoyang Liu1,2, Feng Xu1
1Key Lab of Information Science of Electromagnetic Waves, Fudan University, Shanghai, China.
Frontiers in artificial intelligence
|October 30, 2023
概括
可解释神经网络 (INN) 解决了深度学习模型的黑子性质. 本文将INN分类为模型分解和语义方法,审查进展和未来方向.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 深度神经网络 (DNN) 实现了高性能,但缺乏透明度.
- DNN的"黑子"性质阻碍了对其决策过程的理解.
- 神经网络的可解释性是各个领域的关键研究领域.
研究的目的:
- 审查和分类当前可解释的神经网络 (INN) 方法.
- 探索INN的应用场景和未来发展.
- 为深度学习中的解释性挑战提供全面的概述.
主要方法:
- 将INN方法分为"模型分解"和"语义INN"的分类.
- 模型分解INN将传统的分析模型集成到神经网络层中.
- 语义INN利用可视化和语义信息对黑子模型的后期解释.
主要成果:
- 确定了可解释神经网络的两个主要方向:模型分解和语义INN.
- 模型分解INN进一步根据衍生模型 (数学,物理等) 进行分类. ) 的情况.
- 语义INN采用可视化技术,如卷积层输出可视化和决策树提取.
结论:
- 可解释的神经网络为理解复杂的深度学习模型提供了途径.
- 预设计 (模型分解) 和后 hoc (语义) 方法都有助于网络可解释性.
- 需要进一步的研究来应对现有的挑战,并推进INN领域.
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