The Basic Conception：
Case Study：
LeNet-5
AlexNet
To be mensionend：
1.archtecture picture above is crop from krizhevsky's paper, its firster layer which is 224*224 that actually can't output 55*55 volume, so we make the input images as 227*227*3
2.now we no longer use the norm layer because it actully do nothing to make provement
ZFNet
VGGNet
Note:
1. different from AlexNet using so many conv layers with large shape, VGGNet only use 3*3 conv layer
2. as the spatial size is decresing, the number of parameters is increasing.
3. most memory is in early CONV, and most params is in late FC
GoogLeNet
ResNet
AlphaGo
Summary
Reference:
CNN course
[1]CS231n Winter 2016: Lecture 7: Convolutional Neural Networks
https://www.youtube.com/watch?v=LxfUGhug-iQ
CNN case
[2]ImageNet Classification with Deep Convolutional Neural Networks
http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf
[3]MSRA - Deep residual learning
https://www.youtube.com/watch?v=1PGLj-uKT1w
Understand CNN using excel
[4]Architecture of Convolutional Neural Networks (CNNs) demystified
https://www.analyticsvidhya.com/blog/2017/06/architecture-of-convolutional-neural-networks-simplified-demystified/