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Convolutional Neural
Network Tutorial
Part1
Sungjoon Choi
(sungjoon.choi@cpslab.snu.ac.kr)
Overview
2
Part1: TensorFlow Tutorials
Handling images
Logistic regression
Multi-layer perceptron
Part2: Advances in convo...
CNN
3
Convolutional Neural Network
CNNs are basically layers of convolutions followed by
subsampling and dense layers.
Int...
To understand CNN,
4
Zero-
paddingStride Channel
Convolution
5
http://deeplearning.stanford.edu/wiki/index.php/Feature_extraction_using_convolution
Zero-padding
6
What is the size of the input?
What is the size of the output?
What is the size of the filter?
What is the ...
Stride
7
(Left) Stride size: 1
(Right) Stride size: 2
If stride size equals the filter size, there will
be no overlapping.
8
9
10
11
12
13
14
CNN Architectures
15
AlexNet VGG
GoogLeNet ResNet
Top-5 Classification Error
16
AlexNet
AlexNet
18
ReLU
19
Rectified Linear Unit
tanhReLU
Faster Convergence!
VGG
VGG?
21
GoogLeNet
GoogLeNet
23
GoogLeNet
24
22 Layers Deep Network
Efficiently utilized computing resources, “Inception Module”
Significantly outperforms...
Inception module
25
One by one convolution
26
One by one convolution
27
One by one convolution
28
GoogLeNet
29
Network in Network!
ResNet
Deep residual networks
31
152 layers network
1st place on ILSVRM 2015 classification task
1st place on ImageNet detection
...
Degeneration problem
32
CiFAR 100 Dataset
ImageNet
Residual learning building block
33
Residual mapping
34
Basic residual mapping (same dim.)
Basic residual mapping (different dim.)
“But we will show by
experi...
Deeper bottle architecture
35
Dimension reduction
Convolution
Dimension increasement
Experimental results
36
Experimental results
37
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CNN Tutorial

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CNN Tutorial with brief description of AlexNet, VGG, GoogLeNet, and ResNet.

Published in: Engineering

CNN Tutorial

  1. 1. Convolutional Neural Network Tutorial Part1 Sungjoon Choi (sungjoon.choi@cpslab.snu.ac.kr)
  2. 2. Overview 2 Part1: TensorFlow Tutorials Handling images Logistic regression Multi-layer perceptron Part2: Advances in convolutional neural networks CNN basics Four CNN architectures (AlexNet, VGG, GoogLeNet, ResNet) Application1: Semantic segmentation Application2: Object detection Convolutional neural network
  3. 3. CNN 3 Convolutional Neural Network CNNs are basically layers of convolutions followed by subsampling and dense layers. Intuitively speaking, convolutions and subsampling layers works as feature extraction layers while a dense layer classifies which category current input belongs to using extracted features.
  4. 4. To understand CNN, 4 Zero- paddingStride Channel
  5. 5. Convolution 5 http://deeplearning.stanford.edu/wiki/index.php/Feature_extraction_using_convolution
  6. 6. Zero-padding 6 What is the size of the input? What is the size of the output? What is the size of the filter? What is the size of the zero-padding? 𝑛𝑖𝑛 = 5 𝑛 𝑜𝑢𝑡 = 5 𝑛 𝑓𝑖𝑙𝑡𝑒𝑟 = 3 𝑛 𝑝𝑎𝑑𝑑𝑖𝑛𝑔 = 1
  7. 7. Stride 7 (Left) Stride size: 1 (Right) Stride size: 2 If stride size equals the filter size, there will be no overlapping.
  8. 8. 8
  9. 9. 9
  10. 10. 10
  11. 11. 11
  12. 12. 12
  13. 13. 13
  14. 14. 14
  15. 15. CNN Architectures 15 AlexNet VGG GoogLeNet ResNet
  16. 16. Top-5 Classification Error 16
  17. 17. AlexNet
  18. 18. AlexNet 18
  19. 19. ReLU 19 Rectified Linear Unit tanhReLU Faster Convergence!
  20. 20. VGG
  21. 21. VGG? 21
  22. 22. GoogLeNet
  23. 23. GoogLeNet 23
  24. 24. GoogLeNet 24 22 Layers Deep Network Efficiently utilized computing resources, “Inception Module” Significantly outperforms previous methods on ILSVRC 2014
  25. 25. Inception module 25
  26. 26. One by one convolution 26
  27. 27. One by one convolution 27
  28. 28. One by one convolution 28
  29. 29. GoogLeNet 29 Network in Network!
  30. 30. ResNet
  31. 31. Deep residual networks 31 152 layers network 1st place on ILSVRM 2015 classification task 1st place on ImageNet detection 1st place on ImageNet localization 1st place on COCO detection 1st place on COCO segmentation
  32. 32. Degeneration problem 32 CiFAR 100 Dataset ImageNet
  33. 33. Residual learning building block 33
  34. 34. Residual mapping 34 Basic residual mapping (same dim.) Basic residual mapping (different dim.) “But we will show by experiments that the identity mapping is sufficient for addressing the degradation problem and is economical, and thus W is only used when matching dimensions.”
  35. 35. Deeper bottle architecture 35 Dimension reduction Convolution Dimension increasement
  36. 36. Experimental results 36
  37. 37. Experimental results 37

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