Generative Adversarial Networks for me.

1. GAN
Generative Adversarial Networks from scratch
For 수알못(Na)

2. Generative Model

3. Generative Model 1D example
[0, 1]
http://m.blog.naver.com/atelierjpro/22098475
8512

4. Generative Adversarial Nets
GAN started by Ian Goodfellow
[https://arxiv.org/abs/1406.2661]
GAN used for generating realistic data
( usually, for images )

5. Generative Adversarial Nets
By Ian Goodfellow ( 2014. 06 ) OpenAI
https://arxiv.org/pdf/1406.2661.pdf

6. 경찰과 도둑 (유즈맵 아님)

7. GAN : How does it works?
Random NoiseInput : Random Noise
Output: Realistic Image
Discriminator
Network

8. GAN : How does it works?
< Traditional Training Model >

9. GAN : How does it works?
< GAN Training Model > MAIN IDEA : Just showing lot’s of images
Initial
Trained

10. GAN : How does it works?
< GAN Training Model > MAIN IDEA : Just showing lot’s of images

11. Generative Model 1D example
http://m.blog.naver.com/atelierjpro/22098475
8512

12. Generative Model 1D example
http://m.blog.naver.com/atelierjpro/22098475
8512
g = G(z) # z is uniform distribution
Generative Function
Input : Uniform distribution
Output : Real Data

13. Generative Model 1D example
http://m.blog.naver.com/atelierjpro/22098475
8512
g = G(z) # z is uniform distribution
Generative Function : G is neural network
Input : Uniform distribution
Output : Real Data
Not Trained Model
(yellow)

14. Generative Model 1D example
http://m.blog.naver.com/atelierjpro/22098475
8512
(0 || 1) = D(d) # d is data ( from gen func or real data )
Discriminator : D is neural network
Input : Data
Output : real or fake
Black is D ( input from not trained G )
Yellow is G ( not trained ) - 1
Red is input of G
Blue is Real Data
Discriminator가 잘 구분함
( G와 Real이 완전이 분리)

15. Generative Model 1D example
http://m.blog.naver.com/atelierjpro/22098475
8512
(0 || 1) = D(d) # d is data ( from gen func or real data )
Discriminator : D is neural network
Input : Data
Output : real or fake
Black is D ( input from not trained G )
Yellow is G ( not trained ) - 2
Red is input of G
Blue is Real Data
Discriminator가 잘 구분 못함
( G와 Real이 섞여있음)

16. GAN 1D test

17. GAN EXAMPLES
STACKGAN, Pix2Pix etc..

18. STACKGAN MODEL
Text to Image
( not searching from db, drawing from probability distribution )

19. Pix2pix
https://affinelayer.com/pixsrv/
Image to Image

20. CycleGAN
https://github.com/junyanz/CycleGAN
Image to Image
(Zebra는 다른 환경에서 살기 때문에 배경도 Africa처럼 보이는 것을 확인가능)

21. Apple’s First Paper
Learning from Simulated and Unsupervised Images through Adversarial Training
https://arxiv.org/abs/1612.07828
Image to Image
Fake to REAL

22. GAME THEORY 한장 요약
존 폰 노이만 (사기 캐릭)

23. GAN Architecture

24. GAN Architecture
AdamOptimizer(g_loss)
AdamOptimizer(d_loss)
loss = sigmoid_cross_entropy(D_out, labels) # D_out = ( 0 || 1)
p = sigmoid(logits)
loss =cross_entropy(logits, labels * 0.9) # training skill
d_loss = cross_entropy(logits, labels)
g_loss = cross_entropy(logits, flipped_labels
Flipped Labels: 0 -> 1 | 1 -> 0

25. 참고보면 좋은 자료
https://www.slideshare.net/ssuser7e10e4/wasserstein-gan-i : 전 수학을 몰라욧! ( GAN 수학 )
Udacity deep learning nanodegree
https://en.wikipedia.org/wiki/Generative_model ( Thanks WIKI )