Generative Adversarial Network

67 Pages·2017·2.93 MB·English

by  Hung-yi Lee

Generative Adversarial
Network (GAN)
Restricted Boltzmann Machine:
http://speech.ee.ntu.edu.tw/~tlkagk/courses/MLDS_2015_2/Lecture/RBM
%20(v2).ecm.mp4/index.html
Outlook:
Gibbs Sampling:
http://speech.ee.ntu.edu.tw/~tlkagk/courses/MLDS_2015_2/Lecture/MRF
%20(v2).ecm.mp4/index.html
NIPS 2016 Tutorial: Generative
Adversarial Networks
• Author: Ian Goodfellow
• Paper: https://arxiv.org/abs/1701.00160
• Video: https://channel9.msdn.com/Events/Neural-
Information-Processing-Systems-
Conference/Neural-Information-Processing-
Systems-Conference-NIPS-2016/Generative-
Adversarial-Networks
You can find tips for training GAN here:
https://github.com/soumith/ganhacks
Review
http://www.rb139.com/index.ph
p?s=/Lot/44547
Generation
Writing
Poems?
Drawing?
Review: Auto-encoder
As close as possible
NN c NN
o
d
Encoder Decoder
e
Randomly generate c NN
o
Image ?
d
a vector as code Decoder
e
Review: Auto-encoder
2D
NN
code
Decoder
NN
Decoder
1.5
0
-1.5 1.5
−1.5
0
NN
Decoder
Review: Auto-encoder
-1.5 1.5
Auto-encoder
NN NN
input output
Encoder Decoder
code
VAE
Minimize
m
1 reconstruction error
m
2
m
NN 𝑐
3
input 1 NN
Encoder 𝜎 + 𝑐 output
1 exp 2 Decoder
𝑐
𝜎
3
2
𝜎
3
X 𝑐 = 𝑒𝑥𝑝 𝜎 × 𝑒 + 𝑚
𝑖 𝑖 𝑖 𝑖
𝑒
1
From a normal
𝑒
Minimize
2
distribution
𝑒 3
3
2
෍ 𝑒𝑥𝑝 𝜎 − 1 + 𝜎 + 𝑚
𝑖 𝑖 𝑖
Auto-Encoding Variational Bayes,
𝑖=1
https://arxiv.org/abs/1312.6114
Problems of VAE
• It does not really try to simulate real images
code
NN
As close as
Output
Decoder
possible
One pixel difference One pixel difference
from the target from the target
Realistic Fake
The evolution of generation
NN NN NN
Generator Generator Generator
v1 v2 v3
Discri- Discri- Discri-
minator minator minator
v1 v2 v3
Binary
Classifier
Real images: