深度神经网络与人类之间的色彩感知差异
Ethan O Nadler1, Elise Darragh-Ford2, Bhargav Srinivasa Desikan3
1Carnegie Observatories, USA; Department of Physics, University of Southern California, USA.
Cognition
|September 16, 2023
概括
深度神经网络 (DNN) 不能准确地模拟人类的色彩感知. 一个基于波纹的模型,灵感来自神经科学,比目前的DNN更好地预测人类的颜色判断.
科学领域:
- 计算神经科学是一种神经科学.
- 计算机视觉 计算机视觉
- 认知科学 认知科学
背景情况:
- 深度神经网络 (DNN) 显示出作为人类视觉模型的希望.
- 然而,它们捕捉人类色彩感知的能力尚未得到充分理解.
研究的目的:
- 评估DNN中颜色表示的感知连贯性.
- 将DNN与基于波纹的模型进行比较,以预测人类颜色相似性判断.
主要方法:
- 开发了新的实验来评估DNN颜色嵌入.
- 通过在线调查收集了人类颜色相似性的判断.
- 将DNN (CNN,视觉转换器) 和使用多种图像集的波形分解模型进行比较.
主要成果:
- 最先进的DNN在各种图像类型中显示出与人类颜色判断的显著分歧.
- 在风格转移上训练的卷积DNN显示了一些连贯性,但波纹模型表现更好.
- 结果在不同的DNN任务和层中一致.
结论:
- 目前的DNN不是人类色彩视觉的感知连贯模型.
- 一个基于波纹的模型可以更准确地预测人类的色彩感知.
- 这些发现提升了对机器感知及其认知可信性的理解.
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