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Image style transfer and iOS CoreML, Vision Frameworks
- 1. Image Style Transfer 李智揚 Eric Li @ AIA
- 2. Agenda • Introduction • Method • Discussion • Demo
- 3. Why?
- 4. Papers • A Neural Algorithm of Artistic Style (2015) • Image Style Transfer Using Convolutional Neural Networks (2016) Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
- 8. Introduction • Texture transfer • Manipulation • previous: pixel representation • this: feature spaces • VGG Model • Learn high level features • Without fully connected layers
- 9. Content Reconstruction conv1 conv2 conv3 conv4 conv5
- 10. Style Reconstruction conv1 conv2 conv3 conv4 conv5
- 11. CNN Concept
- 12. Methods
- 13. Content Loss • layer • : filter count • : feature map size ( w x h ) w h
- 14. Content Loss cont. • layer • : filter count • : feature map size ( w x h ) • : content image • : generated image filter position feature map
- 15. Style Loss • layer • : filter count • : feature map size ( w x h ) • Gram matrix
- 16. Gram Matrix a1 a2 a3 =
- 17. Style Loss cont. • layer • : filter count • : feature map size ( w x h ) • : style image • : generated image gram matrix
- 18. Loss
- 22. Result •α/β : content / style 10^-4 10^-2 10^-1 10^-3
- 23. Result cont. • content layer conv2_2 conv4_2
- 25. Style Reconstruction conv1 conv2 conv3 conv4 conv5 L = 2: 0.5, 0,5 L = 5: 0.2, 0.2, 0.2, 0.2, 0.2
- 26. Discussion • Performance • Noise for both photograph -> CV • Separate style and content from pixel • Human ability to abstract content from style
- 27. Other Papers •Perceptual Losses for Real-Time Style Transfer and Super-Resolution
- 28. Other Papers •Controlling Perceptual Factors in Neural Style Transfer
- 29. Other Papers •Deep Photo Style Transfer
- 30. Other Papers •Unpaired Image-to-Image Translation using Cycle- Consistent Adversarial Networks
- 31. Other Papers •Deep painterly harmonization
- 34. 1 3 12 24
- 36. – Steve Jobs “One more thing”
- 41. What’s WWDC • Apple Worldwide Developer Conference • Sessions • Hands-On Labs • Consultation • Get Togethers • Tickets: 1500 USD
- 42. iOS 11 (2017) • CoreML • Vision
- 43. CoreML
- 44. Why CoreML?
- 46. CoreML cont.
- 47. CoreML cont. • Pros • Easy to use • Privacy • Edge computing • Cons • Predict only • Pre-trained model
- 48. CoreML Models • MobileNet • SqueezeNet • Places205-GooLeNet • ResNet50 • Inception V3 • VGG16 learning rate 0.0001
- 50. iOS 11 (2017) • CoreML • Vision
- 57. Purpose
- 59. deepart.io
- 60. Thanks ! Q & A
- 61. Ref. • deepart.io • https://developer.apple.com/documentation/coreml • https://towardsdatascience.com/creating-an-ios-app-with-core-ml-from- scratch-b9e13e8af9cb • https://github.com/keras-team/keras/blob/master/examples/ neural_style_transfer.py • https://hackernoon.com/diy-prisma-fast-style-transfer-app-with-coreml-and- tensorflow-817c3b90dacd • https://github.com/mdramos/fast-style-transfer-coreml • https://github.com/yusuketomoto/chainer-fast-neuralstyle • https://github.com/jcjohnson/fast-neural-style
- 62. • Images • https://www.propelrr.com/wp-content/uploads/2014/07/monkey.jpg • https://i.imgflip.com/ao7ys.jpg • https://en.wikipedia.org/wiki/The_Starry_Night • https://www.slideshare.net/ckmarkohchang/applied-deep-learning-1103- convolutional-neural-networks p.60 • https://qph.ec.quoracdn.net/main-qimg-83c7dee9e8b039c3ca27c8dd91cacbb4 • http://aiacademy.tw/tech-leader-lecturers/ • https://devcentral.f5.com/Portals/0/Users/038/38/38/warning_-_math_ahead.png • https://vaetas.cz/posts/intro-convolutional-neural-networks/ • https://pic4.zhimg.com/80/v2-b72ead36f2277ff9032a9f6b43faa633_hd.jpg • http://fr.memegenerator.net/instance/76960928/woman-in-a-field-of-flowers-no- more-math • https://kknews.cc/zh-hk/sports/gvej5ey.html • https://thumbs.dreamstime.com/z/cleaning-up-data-20120603.jpg • https://www.hercampus.com/school/mt-holyoke/6-quotes-will-get-you-through- midterm-week • https://twitter.com/polkaerospace/status/478533933827883008