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Image Style Transfer and AI on iOS Mobile App
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Image Style Transfer and AI on iOS Mobile App
1.
Image Style Transfer TA
Eric 李智揚 @ Taiwan AI Academy
2.
3.
Agenda • Introduction • Methods •
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
6.
Intuition Content Style
7.
Reconstruction ? Style Content +
8.
Prerequisite conv1 conv2 conv3 conv4 conv5 VGG19 VGG16
9.
Introduction • Texture transfer •
Manipulation • previous: pixel representation • this: feature spaces • VGG Model • Learn high level features • Without fully connected layers
10.
Content Reconstruction conv1 conv2
conv3 conv4 conv5
11.
Style Reconstruction conv1 conv2
conv3 conv4 conv5
12.
CNN 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
20.
21.
22.
23.
Result •α/β : content
/ style 10^-4 10^-2 10^-1 10^-3
24.
Result cont. • content
layer conv2_2 conv4_2
25.
Result cont. • Initialization
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 • Separate
style and content from pixel • Human ability to abstract content from style • Keras example
28.
Other Papers •Perceptual Losses
for Real-Time Style Transfer and Super-Resolution Keras example
29.
Other Papers •Controlling Perceptual
Factors in Neural Style Transfer
30.
Other Papers •Deep Photo
Style Transfer
31.
Other Papers •Deep painterly
harmonization
32.
Other Papers •Unsupervised Cross-Domain
Image Generator
33.
Other Papers •Unsupervised Cross-Domain
Image Generator
34.
Other Papers •Unpaired Image-to-Image
Translation using Cycle- Consistent Adversarial Networks source: 李宏毅
35.
Other Papers •Unpaired Image-to-Image
Translation using Cycle- Consistent Adversarial Networks
36.
Other Papers •StarGAN: Unified
Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
37.
Other Papers •Diverse Image-to-Image
Translation via Disentangled Representations (DRIT)
38.
Other Papers •Diverse Image-to-Image
Translation via Disentangled Representations (DRIT)
39.
40.
41.
42.
1 3 12
24
43.
44.
– Steve Jobs “One
more thing”
45.
46.
47.
Black box v.s. Handcrafted NN
48.
Apple Timeline WWDC June beta Sep.
49.
What’s WWDC • Apple
Worldwide Developer Conference • Sessions • Hands-On Labs • Consultation • Get Togethers • Tickets: 1500 USD
50.
iOS 11 (2017) •
CoreML • Vision
51.
CoreML
52.
Why CoreML? Privacy Latency
53.
54.
CoreML cont.
55.
CoreML cont. • Pros •
Easy to use • Privacy • Edge computing • Cons • Predict only • Pre-trained model
56.
CoreML Models • MobileNet •
SqueezeNet • Places205-GooLeNet • ResNet50 • Inception V3 • VGG16 learning rate 0.0001
57.
https://www.youtube.com/watch?v=RjIbiAC8cBk
58.
iOS 11 (2017) •
CoreML • Vision
59.
Vision Face Detection
60.
Vision Face Landmarks
61.
Vision Image Registration
62.
Vision Rectangle Detection
63.
Vision Text Detection
64.
Vision Object Tracking
65.
Purpose
66.
Apps DeepArt.io Prisma Pikazo
67.
deepart.io
68.
Thanks ! Q &
A
69.
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
70.
• 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
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