电子科技大学研究生《机器学习》精品课程 第14讲深度CNN 14 Deep CNN 郝家胜(Jiasheng Hao) Ph.D.,Associate Professor Email:hao@uestc.edu.cn School of Automation Engineering University of Electronic Science and Technology of China,Chengdu 611731 Aug.2015第一稿；Apr2021第四稿
电子科技大学研究生《机器学习》精品课程 Email: hao@uestc.edu.cn School of Automation Engineering University of Electronic Science and Technology of China, Chengdu 611731 郝家胜 (Jiasheng Hao) Ph.D., Associate Professor Aug. 2015 第一稿；Apr. 2021 第四稿 第14讲 深度CNN 14 Deep CNN
LeNet:( CNN C3:f.maps 16@10x10 INPUT C1:feature maps S4:f.maps 16@5x5 6@28x28 32x32 S2:f.maps 6@14x14 C5:layer F6:layer 120 OUTPUT 84 10 Full connection Gaussian connections Convolutions Subsampling Convolutions Subsampling Full connection CNNs are basically layers of convolutions followed by su bsampling and fully connected layers. 口三大关键思想 ■ 局部感受野（卷积操作) ■ 权值共享 ■ 池化 2
LeNet：CNN 2 CNNs are basically layers of convolutions followed by su bsampling and fully connected layers. o 三大关键思想 n 局部感受野（卷积操作） n 权值共享 n 池化
CNNs in ilsvro Participation and Performance 0.28 0.66 5 0.03 35 0.23 29 2010 2011 2012 2013 2014 2015 2016 Number of Classification Average Precision Entries Errors(top-5) For Object Detection 3
CNNs in ILSVRC 3
What can CNN do perso car person dog norse 0.5 02 城市道 0.3 9, 050 nx k representation of Comvolutional layer with 0515225335445 sentence with static and multiple finer widths and non-stalic channels eature map到 x10
What can CNN do
What can CNN do
5 What can CNN do