根据特征进行扩展和转移:通过捕获特征的独立信息来提高神经网络的概括能力
Tongfeng Sun1, Xiurui Wang1, Zhongnian Li1
1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China; Mine Digitization Engineering Research Centre of Ministry of Education of the People's Republic of China, Xuzhou 221116, China.
概括
本研究引入了神经网络的特征智能缩放和转移 (FwSS),提高了它们捕获独立特征信息的能力. FwSSNet 提高了深度学习模型的概括性和准确性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 神经网络主要通过权重和偏差捕获相关信息.
- 在标准的神经网络架构中,独立的特征信息一直没有得到充分的探索.
研究的目的:
- 引入特征智能缩放和转移 (FwSS) 来捕获独立的特征信息.
- 提出一个新的神经网络架构,FwSSNet,整合FwSS.
- 提高神经网络的概括能力和准确性.
主要方法:
- 在每个网络层输入之前,结合一对规模和转移参数,消除偏差.
- 将FwSS参数初始化为1和0,以单独的学习速度进行训练.
- 将FwSS与批量规范化 (BN) 统一,以创建FwSSNet与BN.
主要成果:
- FwSS有效地捕获独立特征信息以及相关信息.
- FwSSNet在完全连接和深层卷积神经网络中展示了改进的概括能力.
- 在UCI存储库和CIFAR-10数据集上的实验显示,FwSSNets的准确性更高.
结论:
- 功能智能缩放和转移 (FwSS) 是提高神经网络性能的一种有价值的技术.
- 通过更好地利用功能信息,FwSSNet提供了一种有前途的方法来增强深度学习模型.
- 拟议的方法有助于推进神经网络架构,以提高准确性和概括性.
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