在图像分类中,大脑引导的多重传输可以提高尖端神经网络在图像分类中的性能
Zahra Imani1, Mehdi Ezoji2, Timothée Masquelier3
1Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.
Journal of computational neuroscience
|September 18, 2023
概括
这项研究介绍了一种使用尖端神经网络 (SNN) 来进行图像分类的脑引导系统. 通过将SNN视觉特征转移到基于EEG的空间,它显著提高了分类准确性.
科学领域:
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 尖端神经网络 (SNN) 是生物启发的计算模型.
- 传统的神经网络在特征提取和分类准确性方面面临挑战.
- 将SNN与大脑启发的功能空间集成,提供了一种新的方法.
研究的目的:
- 开发一种由大脑引导的系统,使用SNN进行图像分类.
- 提高基于SNN的视觉特征的可分离性和区分能力.
- 通过利用基于EEG的特征空间来提高图像分类的准确性.
主要方法:
- 使用浅层SNN作为一个显式图像解码器.
- 采用基于LSTM的EEG编码器来创建一个歧视性的EEG特征空间.
- 应用了分组转移 (Mk-NN MT) 来将SNN视觉特征映射到EEG特征空间.
- 使用转换方法训练基于SNN的图像编码器.
主要成果:
- 大脑引导系统改善了SNN视觉特征的分离性.
- 将SNN特征映射到基于LSTM的EEG特征空间增强分类.
- 在ImageNet-EEG数据集上,图像分类准确度提高了14.25%.
- 在测试阶段,在不需要EEG信号的情况下证明了有效的分类.
结论:
- 在脑引导系统中嵌入SNN可以提高图像分类性能.
- 多重转移是将SNN与生物启发的特征空间集成的一个可行的技术.
- 拟议的方法显示了对高效的图像分类的希望,特别是在有限的培训数据的情况下.
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