在深度神经网络中识别新兴概念的方法
1University of Bern, Institute of Philosophy, Länggassstrasse 49a, 3012 Bern, Switzerland.
Patterns (New York, N.Y.)
|July 6, 2023
概括
深度神经网络 (DNN) 学习概念,但由于基于实例的概念定义,目前的检测方法不可靠. 结合方法和使用合成数据可以提高理解DNN概念形成的可靠性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 认知科学 认知科学
背景情况:
- 深度神经网络 (DNN) 拥有内部表征,可以学习概念.
- 在DNN中检测概念的现有方法包括网络剖析,特征可视化和概念激活矢量 (TCAV).
研究的目的:
- 批判性地评估当前用于在DNN中检测概念的方法.
- 强调基于实例的概念规范的局限性,并提出解决方案.
- 探索DNN中概念空间的形成及其相关的权衡.
主要方法:
- 讨论和批判性分析现有的概念检测技术 (网络剖析,特征可视化,TCAV).
- 探索实例选择在概念定义中的作用.
- 考虑结合方法并使用合成数据集以提高可靠性.
- 通过准确性-压缩权衡来塑造概念空间的分析.
主要成果:
- 目前的方法为DNN学习非碎的概念关系提供了证据.
- 基于实例的概念规范导致不确定性和不可靠性.
- 结合方法和使用合成数据可以部分减轻不可靠性.
- 概念空间对于理解概念形成至关重要,但缺乏专门的研究方法.
结论:
- 虽然DNN学习概念,但当前的检测方法具有固有的局限性.
- 提高概念检测可靠性需要解决实例不确定性.
- 需要进一步的研究来开发可靠的方法来研究DNN中的概念空间.
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