读convolutional Neural Networks Applied to House Numbers Digit Classification 的收获。

本文以下内容来自读论文以后认为有价值的地方，论文来自：convolutional Neural Networks Applied to House Numbers Digit Classification 。

对于房门号的数字识别问题，文中提出的方法是基于卷积神经网络的，卷积神经网络集特征提取与目标分类于一体，这一点有别于传统的识别方法（传统方法中一般都是基于人工设计的特征提取器，然后把提取到的特征输入给分类器）。

文中在传统的卷积神经网络基础上有两点改进：

第一：pooling层，传统的方法的pooling层一般都为max pooling 或着 average pooling方法，而文中采用的方法为：Lp—pooling，这一里，要用到高斯核。具体可以看论文A theoretical analysis of feature pooling in vision algorithms.。

第二点：Multi-stage features。传统方法中一般都是选择把最后一stage的特征输入给分类器，而本文中采用的方法为：把每stage的特征都输入给分类器，这就是MS，而传统的方法为single-stage feature(SS). 在本文听效果不是很明显。

文中不足点：没有说明文中采用的激活函数为什么啊，即没说说明non-linearity的问题。

一个重要的资源：EBLearn C++ open-source framework . eblearn.sf.net

时间： 2024-11-08 10:14:26

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