零射击神经解码与半监督的多视图嵌入
Yusuke Akamatsu1, Keisuke Maeda2, Takahiro Ogawa2
1Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
这项研究引入了一种新的半监督多视图嵌入方法,用于从fMRI数据中实现零射击的神经解码. 该方法通过解决投影域转移问题来提高未经训练的图像类别的解码精度.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 零射击神经解码旨在识别来自大脑活动 (fMRI) 的图像类别,而无需先前的训练数据.
- 缺乏足够的fMRI数据导致模型概括性差,并导致新类别的投影域转移问题.
研究的目的:
- 提出一种新的零射击神经解码方法,使用半监督的多视图嵌入.
- 为了解决投影域转移问题并增强概括能力.
主要方法:
- 使用了一种半监督方法,结合了没有fMRI数据的附加相关图像.
- 将预测的fMRI活动模式嵌入到多视图嵌入空间 (视觉和语义特征).
主要成果:
- 提出的方法有效地纠正了投影域转移问题.
- 实验结果显示,与现有的零射击神经解码方法相比,性能优越.
结论:
- 半监督的多视图嵌入是改善零射击神经解码的有希望的策略.
- 这种方法提高了从fMRI数据中解码新型图像类别的能力.
关键词:
贝叶斯的推理 贝叶斯的推理功能磁共振成像 (fMRI) 是一种功能性磁共振成像.生成型模型的生成型模型.多视图学习学习多视图学习神经解码的神经解码概率模型是一种概率模型.半监督学习 半监督学习零射击学习的学习更多相关视频
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