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Lecture 9: Convolutional Neural Networks 2 CS109B Data Science 2 Pavlos Protopapas and Mark Glickman 1 Outline 1. Review from last lecture 2. BackProp of MaxPooling layer 3. A bit of history 4. Layers Receptive Field 5. Saliency maps 6.

  1. Lecture 9: Convolutional Neural Networks 2 CS109B Data Science 2 Pavlos Protopapas and Mark Glickman 1

  2. Outline 1. Review from last lecture 2. BackProp of MaxPooling layer 3. A bit of history 4. Layers Receptive Field 5. Saliency maps 6. Transfer Learning 7. CNN for text analysis (example) CS109B, P ROTOPAPAS , G LICKMAN 2

  3. Outline 1. Review from last lecture 2. BackProp of MaxPooling layer 3. A bit of history 4. Layers Receptive Field 5. Saliency maps 6. Transfer Learning 7. CNN for text analysis (example) CS109B, P ROTOPAPAS , G LICKMAN 3

  4. From last lecture + ReLU + ReLU CS109B, P ROTOPAPAS , G LICKMAN 4

  5. Examples I have a convolutional layer with 16 3x3 filters that takes an RGB • image as input. • What else can we define about this layer? • Activation function • Stride • Padding type • How many parameters does the layer have? 16 x 3 x 3 x 3 + 16 = 448 Number of Biases (one Number of Size of per filter) filters channels of Filters prev layer CS109B, P ROTOPAPAS , G LICKMAN 5

  6. Examples Let C be a CNN with the following disposition: • • Input: 32x32x3 images Conv1: 8 3x3 filters, stride 1, padding=same • • Conv2: 16 5x5 filters, stride 2, padding=same Flatten layer • • Dense1: 512 nodes Dense2: 4 nodes • • How many parameters does this network have? (8 x 3 x 3 x 3 + 8) + (16 x 5 x 5 x 8 + 16) + (16 x 16 x 16 x 512 + 512) + (512 x 4 + 4) Conv1 Conv2 Dense1 Dense2 CS109B, P ROTOPAPAS , G LICKMAN 6

  7. What do CNN layers learn? Each CNN layer learns filters of increasing complexity. • • The first layers learn basic feature detection filters: edges, corners, etc. • The middle layers learn filters that detect parts of objects. For faces, they might learn to respond to eyes, noses, etc. • The last layers have higher representations: they learn to recognize full objects, in different shapes and positions. CS109B, P ROTOPAPAS , G LICKMAN 7

  8. CS109B, P ROTOPAPAS , G LICKMAN 8

  9. 3D visualization of networks in action http://scs.ryerson.ca/~aharley/vis/conv/ https://www.youtube.com/watch?v=3JQ3hYko51Y CS109B, P ROTOPAPAS , G LICKMAN 9

  10. Outline 1. Review from last lecture 2. BackProp of MaxPooling layer 3. A bit of history 4. Layers Receptive Field 5. Saliency maps 6. Transfer Learning 7. CNN for text analysis (example) CS109B, P ROTOPAPAS , G LICKMAN 10

  11. Backward propagation of Maximum Pooling Layer Forward mode, 3x3 stride 1 2 4 8 3 6 9 3 4 2 5 5 4 6 3 1 2 3 1 3 4 2 7 4 5 7 CS109B, P ROTOPAPAS , G LICKMAN 11

  12. Backward propagation of Maximum Pooling Layer Forward mode, 3x3 stride 1 2 4 8 3 6 9 3 4 2 5 9 5 4 6 3 1 2 3 1 3 4 2 7 4 5 7 CS109B, P ROTOPAPAS , G LICKMAN 12

  13. Backward propagation of Maximum Pooling Layer Forward mode, 3x3 stride 1 2 4 8 3 6 9 3 4 2 5 9 8 5 4 6 3 1 2 3 1 3 4 2 7 4 5 7 CS109B, P ROTOPAPAS , G LICKMAN 13

  14. Backward propagation of Maximum Pooling Layer Forward mode, 3x3 stride 1 2 4 8 3 6 9 3 4 2 5 9 8 8 5 4 6 3 1 2 3 1 3 4 2 7 4 5 7 CS109B, P ROTOPAPAS , G LICKMAN 14

  15. Backward propagation of Maximum Pooling Layer Forward mode, 3x3 stride 1 2 4 8 3 6 9 3 4 2 5 9 8 8 5 4 6 3 1 9 6 6 7 7 7 2 3 1 3 4 2 7 4 5 7 CS109B, P ROTOPAPAS , G LICKMAN 15

  16. Backward propagation of Maximum Pooling Layer Backward mode. Large fonts represents the values of the derivatives of the current layer (max-pool) and small font the corresponding value of the previous layer. 2 4 8 3 6 9 3 4 2 5 1 9 3 8 1 8 5 4 6 3 1 1 9 4 6 2 6 6 7 2 7 1 7 2 3 1 3 4 2 7 4 5 7 CS109B, P ROTOPAPAS , G LICKMAN 16

  17. Backward propagation of Maximum Pooling Layer Backward mode. Large fonts represents the values of the derivatives of the current layer (max-pool) and small font the corresponding value of the previous layer. 2 4 8 3 6 9 3 4 2 5 1 9 3 8 1 8 5 4 6 3 1 1 9 4 6 2 6 6 7 2 7 1 7 2 3 1 3 4 2 7 4 5 7 CS109B, P ROTOPAPAS , G LICKMAN 17

  18. Backward propagation of Maximum Pooling Layer Backward mode. Large fonts represents the values of the derivatives of the current layer (max-pool) and small font the corresponding value of the previous layer. 2 4 8 3 6 9 3 4 2 5 1 9 3 8 1 8 5 4 6 3 1 1 9 4 6 2 6 6 7 2 7 1 7 2 3 1 3 4 2 7 4 5 7 CS109B, P ROTOPAPAS , G LICKMAN 18

  19. Backward propagation of Maximum Pooling Layer Backward mode. Large fonts represents the values of the derivatives of the current layer (max-pool) and small font the corresponding value of the previous layer. 2 4 8 3 6 +1 9 3 4 2 5 1 9 3 8 1 8 5 4 6 3 1 1 9 4 6 2 6 6 7 2 7 1 7 2 3 1 3 4 2 7 4 5 7 CS109B, P ROTOPAPAS , G LICKMAN 19

  20. Backward propagation of Maximum Pooling Layer Backward mode. Large fonts represents the values of the derivatives of the current layer (max-pool) and small font the corresponding value of the previous layer. 2 4 8 3 6 +1 9 3 4 2 5 1 9 3 8 1 8 5 4 6 3 1 1 9 4 6 2 6 6 7 2 7 1 7 2 3 1 3 4 2 7 4 5 7 CS109B, P ROTOPAPAS , G LICKMAN 20

  21. Backward propagation of Maximum Pooling Layer Backward mode. Large fonts represents the values of the derivatives of the current layer (max-pool) and small font the corresponding value of the previous layer. +3 2 4 8 3 6 +1 9 3 4 2 5 1 9 3 8 1 8 5 4 6 3 1 1 9 4 6 2 6 6 7 2 7 1 7 2 3 1 3 4 2 7 4 5 7 CS109B, P ROTOPAPAS , G LICKMAN 21

  22. Backward propagation of Maximum Pooling Layer Backward mode. Large fonts represents the values of the derivatives of the current layer (max-pool) and small font the corresponding value of the previous layer. +3 2 4 8 3 6 +1 9 3 4 2 5 1 9 3 8 1 8 5 4 6 3 1 1 9 4 6 2 6 6 7 2 7 1 7 2 3 1 3 4 2 7 4 5 7 CS109B, P ROTOPAPAS , G LICKMAN 22

  23. Backward propagation of Maximum Pooling Layer Backward mode. Large fonts represents the values of the derivatives of the current layer (max-pool) and small font the corresponding value of the previous layer. +3 2 4 8 3 6 +1 9 3 4 2 5 1 9 3 8 1 8 5 4 6 3 1 1 9 4 6 2 6 6 7 2 7 1 7 2 3 1 3 4 2 7 4 5 7 CS109B, P ROTOPAPAS , G LICKMAN 23

  24. Backward propagation of Maximum Pooling Layer Backward mode. Large fonts represents the values of the derivatives of the current layer (max-pool) and small font the corresponding value of the previous layer. +4 2 4 8 3 6 +1 9 3 4 2 5 1 9 3 8 1 8 5 4 6 3 1 1 9 4 6 2 6 6 7 2 7 1 7 2 3 1 3 4 2 7 4 5 7 CS109B, P ROTOPAPAS , G LICKMAN 24

  25. Outline 1. Review from last lecture 2. BackProp of MaxPooling layer 3. A bit of history 4. Layers Receptive Field 5. Saliency maps 6. Transfer Learning 7. CNN for text analysis (example) CS109B, P ROTOPAPAS , G LICKMAN 25

  26. Initial ideas • The first piece of research proposing something similar to a Convolutional Neural Network was authored by Kunihiko Fukushima in 1980, and was called the NeoCognitron 1 . • Inspired by discoveries on visual cortex of mammals. Fukushima applied the NeoCognitron to hand-written character • recognition. • End of the 80’s: several papers advanced the field Backpropagation published in French by Yann LeCun in 1985 (independently • discovered by other researchers as well) • TDNN by Waiber et al., 1989 - Convolutional-like network trained with backprop. • Backpropagation applied to handwritten zip code recognition by LeCun et al., 1989 1 K. Fukushima. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4): 93-202, 1980. CS109B, P ROTOPAPAS , G LICKMAN 26

  27. LeNet November 1998: LeCun publishes one of his most recognized papers • describing a “ modern ” CNN architecture for document recognition, called LeNet 1 . Not his first iteration, this was in fact LeNet-5, but this paper is the • commonly cited publication when talking about LeNet. 1 LeCun, Yann, et al. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86.11 (1998): 2278-2324. CS109B, P ROTOPAPAS , G LICKMAN 27

  28. AlexNet • Developed by Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton at Utoronto in 2012. More than 25000 citations. • Destroyed the competition in the 2012 ImageNet Large Scale Visual Recognition Challenge. Showed benefits of CNNs and kickstarted AI revolution. AlexNet • top-5 error of 15.3%, more than 10.8 percentage points lower than runner-up. Main contributions: • Trained on ImageNet with data • augmentation Increased depth of model, GPU • training ( five to six days ) Smart optimizer and Dropout layers • ReLU activation! • CS109B, P ROTOPAPAS , G LICKMAN 28

  29. ZFNet Introduced by Matthew Zeiler and Rob Fergus from NYU, won ILSVRC • 2013 with 11.2% error rate. Decreased sizes of filters. • Trained for 12 days. Paper presented a visualization technique named Deconvolutional • Network , which helps to examine different feature activations and their relation to the input space. CS109B, P ROTOPAPAS , G LICKMAN 29

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