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深度学习入门 : 基于Python的理论与实现
book

深度学习入门 : 基于Python的理论与实现

by 斋藤康毅
July 2018
Intermediate to advanced
310 pages
8h 21m
Chinese
Posts & Telecom Press
Content preview from 深度学习入门 : 基于Python的理论与实现
8.2  深度学习的小历史 
243
袭成为一个转折点,在之后的比赛中,深度学习一直活跃在舞台中央。本节
我们以
ILSVRC
这个大规模图像识别比赛为轴,看一下深度学习最近的发展
趋势。
8.2.1
 ImageNet
ImageNet
[25]
是拥有超过100 万张图像的数据集。如图8
-
7 所示,它包含
了各种各样的图像,并且每张图像都被关联了标签(类别名)。每年都会举办
使用这个巨大数据集的
ILSVRC
图像识别大赛。
mammal
vehicle craft watercraft sailing vessel sailboat trimaran
placental carnivore canine dog working dog husky
8-7 大规模数据集ImageNet 的数据例(引用自文献[25]
ILSVRC
大赛有多个测试项目,其中之一是“类别分类”(
classification
),
在该项目中,会进行1000 个类别的分类,比试识别精度。我们来看一下最
近几年的
ILSVRC
大赛的类别分类项目的结果。图8
-
8 中展示了从2010 年到
2015 年的优胜队伍的成绩。这里,将前 5 类中出现正确解的情况视为“正确”,
此时的错误识别率用柱形图来表示。
8
-
8 中需要注意的是,以2012 年为界,之后基于深度学习的方法一直
居于首位。实际上,我们发现 2012 年的
AlexNet
大幅降低了错误识别率。并且,
此后基于深度学习的方法不断在提升识别精度。特别是2015 年的
ResNet
(一
个超过150
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Publisher Resources

ISBN: 9787115485588