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Deep Learning behind Prisma

An introduction to image style transfer using deep convolutional neural network

Deep Learning behind Prisma

  1. 1. Deep Learning behind Prisma ——Image style transfer with Convolutional Neural Network lostleaf
  2. 2. Agenda • Introduce deep learning models for image style transfer via recent papers • Prisma is kind of a stunt, but it should have used similar techniques • Agenda • A brief introduction to convolutional neural network • Neural style • Real-Time Style Transfer
  3. 3. Prisma • An Russian mobile app • Turns your photos into awesome artworks • With Deep Learning!!! Hotel Ukraine rendered by Prisma from Premier Medvedev’s Instagram
  4. 4. Image Style Transfer + Arch Starry Night (van Gogh) Arch painted by van Gogh
  5. 5. A brief introduction to convolutional neural network Some of the images are from Prof. Feifei Li’s lecture notes
  6. 6. Neuron • w: weight, b: bias
  7. 7. Activation function(common ones) Thresholding, preferred in modern network structures Slower: exponentials Harder to train: vanishing gradient Activation function: nonlinear functions
  8. 8. Fully connected neural network
  9. 9. Convolution • The brown numbers in the yellow part is called conventional kernel / filter • Convolve the filter with the image: slide over the image spatially, computing dot products • Right: A 3*3 convolution sums up the diagonals From Prof. Andrew Ng’s UFLDL tutorial
  10. 10. Convolutional layer Filters always extend the full depth of the input volume Why *3? 3 channels: R, G & B
  11. 11. Convolutional layer 1 number: the result of taking a dot product between the filter and a small 5*5*3 chunk of the image
  12. 12. Convolutional layer Transform with activation function f f
  13. 13. Convolutional layer • A convolutional layer consists of several filters • For example, if we had 6 5*5 filters, we’ll get 6 separate activation maps • Stack these up to get a tensor of size 28*28*6 • May add padding to obtain same output size
  14. 14. Why convolution? • Each value could be considered as an output of a neuron • Features of image data: • pixels only related to small neighborhood (local connection) • repeat pattern & content move around (weight sharing) • Reduces the complexity and computation of neural network by utilizing natures of images 6
  15. 15. Pooling Layer • Right: max pooling for example • Operate independently on every depth slice of the input • Reduce the reduce the spatial size of activation map (reduce amount of parameters and computation) • Increase the shift invariance
  16. 16. Case study1: MNIST & Lenet • MNIST handwritten digits recognition • “hello world” of deep learning
  17. 17. Lenet LeCun, Yann, et al. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86.11 (1998): 2278-2324. pooling pooling
  18. 18. Case study2: ImageNet & VggNet • ImageNet: a large image dataset in thousands of classes
  19. 19. VggNet(Vgg19) Image by Mark Chang • Runner-up of Imagenet challenge 2014 • 19 trainable layers • 16 convolutional layers (3*3) • 5 max pooling layers (2*2) • 3 fully connected layers Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
  20. 20. Typical architecture • Convolutional part & Fully connected part • [(CONV-RELU)*N-POOL?]*M-(FC-RELU)*K,SOFTMAX
  21. 21. Neural style Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576 (2015). Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "Image style transfer using convolutional neural networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
  22. 22. Intuition • Convolutional neural networks well trained on large datasets (VggNet) could be a powerful feature extractor, like human brains • Human painters are talented in combining content and style
  23. 23. Goal • Given a content image p and a style image a • Find an image x that • Similar to p in content • Similar to a in style ≈ ? ≈ p a x
  24. 24. Formulation • Use Vgg19(Convolutional part) for feature extraction • Two loss function • Content loss: difference in content between x and p • Style loss: difference in style between x and a • Find an image x that minimize the weighted sum between content and style loss
  25. 25. How to find x Image by Mark Chang
  26. 26. Some results J.M.W. Turner Vincent van Gogh Edvard Munch
  27. 27. con’d Pablo Picasso Wassily Kandinsky
  28. 28. Balance content and style • Weights of content and style: hyper parameter • Search multiple combinations to satisfy personal aesthetic
  29. 29. Photorealistic style transfer New York London
  30. 30. Drawbacks • Iterative optimization • Slow: 65s to render the 600 * 400 arch image with GTX 980M • Power consuming: not acceptable for mobile apps like Prisma
  31. 31. Real-Time Style Transfer
  32. 32. Intuition • Style transfer is essentially a image transformation problem: image in, image out • Generative CNN’s are proved to be powerful in many other image transformation problems
  33. 33. Goal • For a specific style image a, train a CNN that • Accepts a content image p as input • Outputs a synthesized image x has content similar to p and style similar to a
  34. 34. Generative CNN • Pre trained VggNet for formulating the loss function • Style target: a fixed style image, e.g. starry night • Input image & content target: images sampled from a large dataset • Image Transform Net: fully convolutional network (and some fancy new staffs) Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. "Perceptual losses for real-time style transfer and super-resolution." arXiv preprint arXiv:1603.08155 (2016).
  35. 35. Details & Improvements • Image size 256 * 256 • Trained on a large image dataset for 4h with GTX Titan X • 200 ~ 1000X rendering speedup
  36. 36. Some results
  37. 37. Con’d
  38. 38. Comparison • Original neural style: hundreds of optimization iterations • Generative CNN: tens of thousands of training iterations, one forward pass for synthesize • Prisma's offline mode probably uses similar technologies
  39. 39. Parallel work — Texture Network Ulyanov, Dmitry, et al. "Texture Networks: Feed-forward Synthesis of Textures and Stylized Images." arXiv preprint arXiv:1603.03417 (2016).
  40. 40. Take home • What make up a CNN • Convolution, pooling, fully connected layer... • How neural style works • CNN for feature extraction & iterative optimization • Fast style transfer • Train a generative CNN for a specific style
  41. 41. Some open course resources • Introduction to Computer Vision, Udacity • Deep Learning, Udacity • Convolutional Neural Networks for Visual Recognition, Stanford CS231n * • Deep Learning for Natural Language Processing, Stanford CS224d

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