走向忠实的神经网络内在解释与沙普利的附加自我归因
概括
本研究介绍了沙普利增材自我归因神经网络 (SASANet),这是一个自我解释神经网络的新框架. 通过整合Shapley值赋值,SASANet提供了真正的可解释性和增强的模型表达性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 可解释的人工智能 (XAI)
背景情况:
- 当前的自我解释神经网络往往缺乏解释性的理论依据,并限制模型的表达力.
- 现有的方法很难为模型预测提供真正的,理论上合理的解释.
研究的目的:
- 提出一个通用的附加性自我归因 (ASA) 框架,以统一现有方法.
- 引入一个新的沙普利增材自我归因神经网络 (SASANet),该网络包含沙普利值归因,以提高可解释性.
- 通过提高可解释性和性能来解决当前自我解释模型的局限性.
主要方法:
- 开发了一个新的沙普利增材自我归因神经网络 (SASANet).
- 设计了一个使用边际贡献 (MCs) 和内部蒸程序的中间顺序方案.
- 从理论上证明了中间的自我归因值与输出中的沙普利值趋同.
主要成果:
- 在性能方面,SASANet实现了高的可解释性,并超过了现有的自我归因模型.
- 萨萨网的性能与通常使用的封闭盒模型相美.
- 在SASANet中的自我归因方法提供了比后期方法更准确和更有效的解释.
结论:
- SASANet是第一个实现模型智能的Shapley归因的自我解释神经网络结构.
- 拟议的框架提高了神经网络的可解释性和性能.
- 在理论上,SASANet提供了一种合理且实际上有效的方法来解释AI.
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