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Deep Neural Networks for Image-Based Dietary Assessment
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在单像素成像中近似深度学习重建预测的不确定性
Ruibo Shang1,2, Mikaela A O'Brien1, Fei Wang3,4
1Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA.
Communications engineering
|March 11, 2024
概括
这项研究引入了贝叶斯卷积神经网络 (BCNN) 用于单像素成像 (SPI). BCNN量化了预测不确定性,有助于评估图像质量和指导系统调整,以改善深度学习重建.
科学领域:
- 光学和光子学 在光学和光子学.
- 计算机成像成像技术
- 人工智能的人工智能是人工智能.
背景情况:
- 单像素成像 (SPI) 提供高速,宽波长采集和紧的系统.
- 与传统方法相比,深度学习 (DL) 方法提高了SPI中的图像重建质量.
- 量化DL预测中的不确定性对于可靠的图像重建至关重要.
研究的目的:
- 开发贝叶斯卷积神经网络 (BCNN) 用于估计单像素成像中的预测不确定性.
- 为了将 BCNN 衍生的不确定性与 SPI 中的重建错误相关联.
- 在实际的SPI应用中提供一个用于评估DL模型和数据集质量的工具.
主要方法:
- 贝叶斯卷积神经网络 (BCNN) 架构的实施.
- 在SPI数据上训练 BCNN,以预测每个像素的概率分布.
- 评估预测的不确定性与实际重建错误之间的相关性.
主要成果:
- BCNN提供了像素智能的概率分布,表明预测不确定性.
- BCNN的不确定性预测表明与SPI重建错误存在相关性.
- 预测的不确定性水平可以指导系统,数据或网络参数的必要调整.
结论:
- 拟议的BCNN有效量化了基于DL的SPI中的不确定性.
- 这种不确定性估计作为预测信心的可靠指标.
- BCNN促进了SPI应用中的DL模型和数据集的质量评估.
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