CNN Tutorial
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CNN Tutorial with brief description of AlexNet, VGG, GoogLeNet, and ResNet.
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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
- 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
- 26. One by one convolution 26
- 27. One by one convolution 27
- 28. One by one convolution 28
- 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