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Image Style Transfer
TA Eric 

李智揚 

@ Taiwan AI Academy
Agenda
• Introduction

• Methods

• Discussion

• Demo
Why?
Papers
• A Neural Algorithm of Artistic Style (2015)
• Image Style Transfer Using Convolutional Neural
Networks (2016)
Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
Intuition
Content
Style
Reconstruction
?
Style
Content
+
Prerequisite
conv1
conv2
conv3
conv4
conv5
VGG19
VGG16
Introduction
• Texture transfer
• Manipulation
• previous: pixel representation

• this: feature spaces

• VGG Model
• Learn high level features

• Without fully connected layers
Content Reconstruction
conv1 conv2 conv3 conv4 conv5
Style Reconstruction
conv1 conv2 conv3 conv4 conv5
CNN
Concept
Methods
Content Loss
• layer 

• : filter count

• : feature map size ( w x h )
w
h
Content Loss cont.
• layer 

• : filter count

• : feature map size ( w x h )

• : content image 

• : generated image
filter
position
feature map
Style Loss
• layer 

• : filter count

• : feature map size ( w x h )

• Gram matrix
Gram Matrix
a1 a2 a3
=
Style Loss cont.
• layer 

• : filter count

• : feature map size ( w x h )

• : style image

• : generated image
gram matrix
Loss
Result
•α/β : content / style
10^-4
10^-2 10^-1
10^-3
Result cont.
• content layer
conv2_2
conv4_2
Result cont.
• Initialization
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
Discussion
• Performance

• Separate style and content from pixel

• Human ability to abstract content from style

• Keras example
Other Papers
•Perceptual Losses for Real-Time Style Transfer and
Super-Resolution
Keras example
Other Papers
•Controlling Perceptual Factors in Neural Style Transfer
Other Papers
•Deep Photo Style Transfer
Other Papers
•Deep painterly harmonization
Other Papers
•Unsupervised Cross-Domain Image Generator
Other Papers
•Unsupervised Cross-Domain Image Generator
Other Papers
•Unpaired Image-to-Image Translation using Cycle-
Consistent Adversarial Networks
source: 李宏毅
Other Papers
•Unpaired Image-to-Image Translation using Cycle-
Consistent Adversarial Networks
Other Papers
•StarGAN: Unified Generative Adversarial Networks for Multi-Domain
Image-to-Image Translation
Other Papers
•Diverse Image-to-Image Translation via Disentangled
Representations (DRIT)
Other Papers
•Diverse Image-to-Image Translation via Disentangled
Representations (DRIT)
1 3 12 24
– Steve Jobs
“One more thing”
Black box

v.s.

Handcrafted NN
Apple Timeline
WWDC
June
beta
Sep.
What’s WWDC
• Apple Worldwide Developer Conference

• Sessions

• Hands-On Labs

• Consultation

• Get Togethers

• Tickets: 1500 USD
iOS 11 (2017)
• CoreML

• Vision
CoreML
Why CoreML?
Privacy
Latency
CoreML cont.
CoreML cont.
• Pros

• Easy to use

• Privacy

• Edge computing

• Cons

• Predict only

• Pre-trained model
CoreML Models
• MobileNet

• SqueezeNet

• Places205-GooLeNet

• ResNet50

• Inception V3

• VGG16
learning rate 0.0001
https://www.youtube.com/watch?v=RjIbiAC8cBk
iOS 11 (2017)
• CoreML

• Vision
Vision
Face Detection
Vision
Face Landmarks
Vision
Image Registration
Vision
Rectangle Detection
Vision
Text Detection
Vision
Object Tracking
Purpose
Apps
DeepArt.io Prisma Pikazo
deepart.io
Thanks !
Q & A
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
• 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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