Lecture 6: Convolutional Neural Networks
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- POSTECH EECE695J, "딥러닝 기초 및 철강공정에의 활용", 2017-10-12 - Contents: components of convolutional neural network (convolution layer & pooling layer), CNN architectures, implementation of a CNN - Video: https://youtu.be/u07CbFuUpbk
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Lecture 6: Convolutional Neural Networks
- 1. Convolutional Neural Networks Sang Jun Lee Ph.D. candidate, POSTECH Email: lsj4u0208@postech.ac.kr EECE695J 전자전기공학특론J(딥러닝기초및철강공정에의활용) – LECTURE 6 (2017. 10. 12)
- 2. 2 ▣ Lecture 4 & 5: Neural Network I & II 1-page Review Input layer Output layerHidden layer Perceptron Multilayer perceptron (MLP) Backpropagation Activation functions Sigmoid tanh ReLU Leaky ReLU Data preprocessing Weight initialization - RBM (DBN) - Xavier initialization 𝑾𝑾~𝑵𝑵(𝟎𝟎, 𝝈𝝈𝟐𝟐 )/𝒏𝒏 Optimization - Gradient descent - SGD with momentum - AdaGrad - Adam Regularization - model ensemble - dropout
- 3. CNN components - Convolutional layer - Pooling layer CNN architecture - General architecture of CNN - Case study (AlexNet, VGG) CNN implementation Other computer vision tasks - Semantic segmentation 3 Contents
- 4. Traditional Method - Data에 대한 feature vector 설계 - SVM 및 MLP 등의 분류기 사용 및 적은 양의 데이터로 학습 가능 4 Pattern Recognition in Computer Vision Hand-crafted feature extractor Simple trainable classifier data output Feature representation
- 5. Deep learning based Method - Data에 대한 feature representation 및 classifier를 jointly optimize - End-to-end learning algorithm 설계 가능 5 Pattern Recognition in Computer Vision Trainable feature extractor Trainable classifierdata output Black box
- 6. MLP ( = Fully connected layer) - 영상 데이터에 대하여 MLP를 이용하여 network를 구성하면.. - 32x32x3 image ≅ 3072-dimensional vector - 학습 parameter가 증가 → 학습에 필요한 데이터 증가 Convolutional layer - Convolve a filter with an image (preserve spatial structure) - Slide over the image spatially and compute dot products - Parameter sharing & Local connectivity 6 Convolutional layer
- 7. MLP의 구성 - Design parameter: Hidden layer 및 각 layer의 neuron 개수 Convolutional layer - Convolution filter (kernel) size, layer의 개수, 각 layer의 filter 개수 등 7 Convolutional layer 32x32x3 image 5x5x3 filter height width channel Input layer Output layerHidden layer
- 8. Convolutional layer - Convolve a filter with an image (preserve spatial structure) - Slide over the image spatially and compute dot products 8 Convolutional layer 32x32x3 image 5x5x3 filter Convolve (slide) over all spatial locations Activation map (Feature map) Parameter 수 = 5x5x3 +1(bias)
- 9. Consider a second, green filter 9 Convolutional layer 32x32x3 image 5x5x3 filter Convolve (slide) over all spatial locations Activation maps Depth slice
- 10. 5x5 filters 6개 사용 → 6 separate activation maps Activation map의 크기: input size – (kernel size – 1) 10 Convolutional layer Convolution layer
- 11. 11 Convolutional layer Convolutional neural network is a sequence of convolution layers (interspersed with activation functions) CONV ReLU (6개의 5x5x3 filters) CONV ReLU (10개의 5x5x3 filters) CONV ReLU 첫 번째와 두 번째 convolutional layer에 있는 parameter의 수?
- 12. 12 Convolutional layer Feature map의 크기 5x5 convolutional filter 사용 Receptive field - Activation map에서 하나의 값을 결정하는데 사용되는 input에서의 영역 크기 - 5x5 filter로 이루어진 convolutional layer 2개를 통과시킨 activation map의 receptive field = 9x9 12x12 8x8 4x4 CONV 5x5 CONV 5x5
- 13. 13 Convolutional layer Zero-padding - Convolutional layer가 activation map의 크기에 영향을 주지 않도록 하기 위해.. - Pad size = (filter size - 1) / 2 8x8 8x8 Zero-padding: 2
- 14. 14 Convolutional layer Stride - Convolution filter를 얼마만큼 움직일 것인가? 9x9 3x3 5x5 convolution filter with stride 2 Feature map size = (input size + 2x padding – filter size) +1
- 15. 15 Pooling layer Pooling - Activation map의 크기를 효과적으로 줄이기 위한 down sampling (in spatially) - max pooling, average pooling, etc.
- 16. 16 Architecture General architecture : Convolution layer (with an activation function) + Pooling layer의 연속 CNN architecture for classification - Convolutional layer와 pooling layer를 이용하여 spatial domain에서의 feature map size를 1x1로 만드는 것이 핵심 - Output layer의 depth는 classification 하고자 하는 class의 개수와 동일
- 17. 17 Case study AlexNet (2012) 8 layers INPUT – 227x227x3 CONV1 (11x11x3x96 filter with stride 4) – 55x55x96 POOL1 (3x3 pooling with stride 2) – 27x27x96 CONV2 (5x5x96x256 filter with stride 1 and padding 2) – 27x27x256 POOL2 (3x3 pooling with stride 2) – 13x13x256 CONV3 (3x3x256x384 filter with stride 1 and padding 1) – 13x13x384 CONV4 (3x3x384x384 filter with stride 1 and padding 1) – 13x13x384 CONV5 (3x3x384x256 filter with stride 1 and padding 1) – 13x13x384 Fully-connected (4096) Fully-connected (4096) Fully-connected (1000) Feature map size = (input size + 2x padding – filter size) / stride +1 227 227
- 18. 18 Case study AlexNet (2012) ImageNet Large Scale Visual Recognition Challenge (ILSVRC) winners
- 19. 19 Case study VGG (2014) Small filters & Deeper network 왜 3x3의 small filter를 사용하는가? : 3개의 3x3 convolutional layer (with stride 1)를 이용하여 상대적으로 적은 수의 parameter로 7x7 convolutional layer와 동일한 receptive field의 효과를 낼 수 있음
- 20. 20 Implementation of CNN MNIST classification : 28x28 크기의 숫자 영상 데이터
- 21. 21 Implementation of CNN MNIST classification
- 22. 22 Implementation of CNN MNIST classification
- 23. 23 Implementation of CNN MNIST classification
- 24. 24 Implementation of CNN MNIST classification
- 25. 25 Implementation of CNN MNIST classification
- 26. 26 Computer Vision Tasks Image classification Other computer vision tasks Semantic segmentation Localization (single object) Detection (multiple objects) Instance segmentation
- 27. 27 Semantic segmentation - Label each pixel in the image with a category label - Don’t differentiate instances
- 28. 28 Semantic segmentation Sliding window based method
- 29. 29 Semantic segmentation Fully convolutional manner (without down- and up-sampling)
- 30. 30 Semantic segmentation Unpooling Learnable upsampling: deconvolution (transposed convolution)
- 31. 31 Semantic segmentation Fully convolutional manner
- 32. CNN components - Convolutional layer - Pooling layer CNN architecture - General architecture of CNN - Case study (AlexNet, VGG) CNN implementation Other computer vision tasks - Semantic segmentation 32 Summary
- 33. Deep learning for sequential data Recurrent neural network - Vanilla RNN - LSTM (Long short term memory) 33 Preview (Lecture 7)