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精通特征工程
book

精通特征工程

by Alice Zheng, Amanda Casari
April 2019
Intermediate to advanced
172 pages
4h 39m
Chinese
Posts & Telecom Press
Content preview from 精通特征工程
自动特征生成:图像特征提取和深度学习
117
8.3
 通过深度神经网络学习图像特征
要定义良好的图像特征,
SIFT
HOG
还有很长一段路要走。然而,计算机视觉领域的最
新成果则来自于另外一个非常不同的方向:深度神经网络模型。这一突破发生在
2012
ImageNet
大规模视觉识别竞赛(
ILSVRC
)中,当时来自多伦多大学的一群研究者几
乎将前一年优胜者的错误率降低了一半。他们称其所使用的方法为“深度学习”,以强调
这种方法不同于以前的神经网络模型,而是包括很多叠加在一起的神经网络层和转换层
的最新一代模型。
ILSVRC 2012
的优胜模型——后来被称为
AlexNET
,以它的首席发明
者命名——有
13
层(
Krizhevsky
等,
2012
),
ILSVRC 2014
的获胜者
GoogleNet
22
Szegedy
等,
2014
)。
从表面上看,堆叠神经网络的机制似乎与
SIFT
HOG
的图像梯度直方图相去甚远。但从
AlexNet
的可视化可以看出,它的最初几层本质上就是计算边缘梯度和其他一些简单模式,
SIFT
HOG
非常相似。随后的几层将局部模式组合成更全局化的模式。最终结果是一
个特征提取器,比以前的提取器要强大许多。
堆叠神经网络层(或任意其他分类模型)这种模式并不是什么新的思想,但训练这种复杂
模型需要大量的数据和计算能力,这是近期才具备的。
ImageNet
数据集中有
120
万张标
记好的图像,类别有
1000
个。现代
GPU
大大加快了矩阵
-
向量计算,很多机器学习模型
(包括神经网络)的内核就需要这种计算。深度学习方法的成功就依赖于可使用大量数据 ...
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Publisher Resources

ISBN: 9787115509680