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CNN Tutorial
This document provides an overview of convolutional neural networks and summarizes four popular CNN architectures: AlexNet, VGG, GoogLeNet, and ResNet. It explains that CNNs are made up of convolutional and subsampling layers for feature extraction followed by dense layers for classification. It then briefly describes key aspects of each architecture like ReLU activation, inception modules, residual learning blocks, and their performance on image classification tasks.
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CNN Tutorial
- 1. Convolutional Neural Network Tutorial Part1 SungjoonChoi (sungjoon.choi@cpslab.snu.ac.kr)
- 2. Overview 2 Part1: TensorFlow Tutorials Handlingimages 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 CNNsare 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.
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- 6. Zero-padding 6 What is thesize 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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- 19. ReLU 19 Rectified Linear Unit tanhReLU FasterConvergence!
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- 24. GoogLeNet 24 22 Layers DeepNetwork Efficiently utilized computing resources, “Inception Module” Significantly outperforms previous methods on ILSVRC 2014
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- 26. One by oneconvolution 26
- 27. One by oneconvolution 27
- 28. One by oneconvolution 28
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- 31. Deep residual networks 31 152layers 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 100Dataset ImageNet
- 33. Residual learning buildingblock 33
- 34. Residual mapping 34 Basic residualmapping (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 Dimensionreduction Convolution Dimension increasement
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