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Image style transfer & AI on App
- 1. Image Style Transfer & iOS ML Application 李智揚 Sai Li AT071086 @ AIA 707
- 2. About Me • former 有無科技 • now
- 3. Agenda • Introduction • Method • Discussion • Demo
- 4. Why?
- 5. Papers • A Neural Algorithm of Artistic Style (2015) • Image Style Transfer Using Convolutional Neural Networks (2016) • Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
- 9. Introduction • Texture transfer • Previous: manipulate pixel representation • This: manipulate feature spaces • VGG: Learn high level features from object detection • VGG without fully connected layers
- 10. Content Reconstruction conv1 conv2 conv3 conv4 conv5
- 11. Style Reconstruction conv1 conv2 conv3 conv4 conv5
- 12. Concept
- 13. Methods
- 14. Content Loss • layer • : filter count • : feature map size ( w x h ) w h
- 15. Content Loss cont. • layer • : filter count • : feature map size ( w x h ) • : content image • : generated image filter position feature map
- 16. Style Loss • layer • : filter count • : feature map size ( w x h ) • Gram matrix
- 17. Gram Matrix a1 a2 a3 =
- 18. Style Loss cont. • layer • : filter count • : feature map size ( w x h ) • : style image • : generated image gram matrix
- 19. Loss
- 23. Result •α/β : content / style 10^-4 10^-2 10^-1 10^-3
- 24. Result cont. • content layer conv2_2 conv4_2
- 26. 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
- 27. Discussion • Performance • Noise for both photograph -> CV • Separate style and content from pixel • Human ability to abstract content from style What’s next? Perceptual Losses for Real-Time Style Transfer and Super-Resolution
- 30. 1 3 12 24
- 32. – Steve Jobs “One more thing”
- 35. What’s WWDC • Apple Worldwide Developer Conference • Sessions • Hands-On Labs • Consultation • Get Togethers • Tickets: 1500 USD
- 36. iOS 11 (2017) • CoreML • Vision
- 37. CoreML
- 38. Why CoreML?
- 40. CoreML cont.
- 41. CoreML cont. • Pros • Easy to use • Privacy • Edge computing • Cons • Predict only • Pre-trained model
- 42. CoreML Models • MobileNet • SqueezeNet • Places205-GooLeNet • ResNet50 • Inception V3 • VGG16 learning rate 0.0001
- 44. iOS 11 (2017) • CoreML • Vision
- 51. Purpose
- 53. deepart.io
- 54. Thanks ! Q & A https://www.hercampus.com/school/mt-holyoke/6-quotes-will-get-you-through-midterm-week https://twitter.com/polkaerospace/status/478533933827883008
- 55. 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
- 56. • 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