通过在卷积神经网络中结合强度以模糊,改进了人类视觉的建模
Hojin Jang1,2,3, Frank Tong4
1Department of Psychology, Vanderbilt Vision Research Center, Vanderbilt University, Nashville, TN, USA. hojin4671@korea.ac.kr.
Nature communications
|March 5, 2024
概括
用模糊图像训练的卷积神经网络 (CNN) 更好地预测神经反应并改善对象识别. 整合模糊增强了CNN的形状灵敏度和稳定性,使它们更接近人类的感知.
科学领域:
- 神经科学是一个神经科学.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 视觉输入到视网膜往往被外围分辨率限制和光学失焦所降低.
- 标准卷积神经网络 (CNN) 通常在清晰的图像上进行训练,忽视模糊的影响.
- 这种缺乏模糊的训练数据可能会导致CNN过度依赖高空间频率,偏离生物视觉.
研究的目的:
- 调查使用模糊图像训练CNN是否可以提高其性能和生物相关性.
- 测试该假设模糊输入对于强大的对象识别在CNN中至关重要.
- 为了比较标准CNN和CNN在清晰和模糊图像上训练的性能.
主要方法:
- 开发并比较标准的CNN与在数据集上训练的CNN,包括清晰和模糊的图像.
- 评估了CNN在各种观看条件下预测神经对视觉刺激反应的表现.
- 评估了CNN的形状灵敏度,对视觉噪声的强度以及与人类感知的对应性.
主要成果:
- 用模糊图像训练的CNN在预测神经反应方面明显优于标准CNN.
- 经过模糊训练的CNN表现出对塑造信息的增强敏感性.
- 这些CNN对各种形式的视觉噪声表现出更强的稳定性,并与人类感知有了更好的对齐.
结论:
- 模糊的视觉体验对于开发强大的生物视觉系统至关重要.
- 用模糊图像训练CNN可以提高他们对人类视觉处理进行概括和模仿的能力.
- 这项研究提供了神经计算证据,证明了视觉模型中降低视觉输入的重要性.
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