CNN Tutorial

1. Convolutional Neural Network Tutorial Part1 Sungjoon Choi (sungjoon.choi@cpslab.snu.ac.kr)

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. 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. To understand CNN, 4 Zero- paddingStride Channel

5. Convolution 5 http://deeplearning.stanford.edu/wiki/index.php/Feature_extraction_using_convolution

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. Stride 7 (Left) Stride size: 1 (Right) Stride size: 2 If stride size equals the filter size, there will be no overlapping.

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15. CNN Architectures 15 AlexNet VGG GoogLeNet ResNet

16. Top-5 Classification Error 16

17. AlexNet

18. AlexNet 18

19. ReLU 19 Rectified Linear Unit tanhReLU Faster Convergence!

20. VGG

21. VGG? 21

22. GoogLeNet

23. GoogLeNet 23

24. GoogLeNet 24 22 Layers Deep Network Efficiently utilized computing resources, “Inception Module” Significantly outperforms previous methods on ILSVRC 2014

25. Inception module 25

26. One by one convolution 26

29. GoogLeNet 29 Network in Network!

30. ResNet

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. Degeneration problem 32 CiFAR 100 Dataset ImageNet

33. Residual learning building block 33

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. Deeper bottle architecture 35 Dimension reduction Convolution Dimension increasement

36. Experimental results 36

37. Experimental results 37

CNN Tutorial

CNN Tutorial

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