人类的感知学习:一个活跃的,自上而下的指导过程
1Department of Cognitive Psychology, Institute for Brain and Behavior Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands h.a.slagter@vu.nlhttps://research.vu.nl/en/persons/heleen-slagter.
The Behavioral and brain sciences
|December 6, 2023
概括
开发类似人类视觉的深度神经网络 (DNN) 需要超越白板训练. 相反,DNN应该包含自上而下的指导,反映人类感知学习以获得更准确的模型.
科学领域:
- 认知科学 认知科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 目前用于人类视觉的深度神经网络 (DNN) 模型通常使用与视觉图像训练数据的"白板"方法.
- 这种方法与人类感知和感知学习的既定理论形成鲜明对比.
研究的目的:
- 为人类视觉建模提出DNN开发中的转变.
- 在人工视觉系统中整合主动的,自上而下的感知原则.
主要方法:
- 审查关于人类感知和感知学习的文献.
- 基于主动感知原则,对训练DNNs的修订方法进行概念化.
主要成果:
- 标准的DNN培训可能无法完全捕捉类似人类的视觉处理.
- 一种主动的,自上而下的指导方法被认为是开发更类似于人类的DNN视觉模型的关键.
结论:
- 开发精确模拟人类视觉的DNN需要从被动的,自下而上的处理中脱离.
- 未来的DNN架构应采用活跃的,由人类感知学习提供信息的自上而下的引导机制.
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