This document provides an overview of generative adversarial networks (GANs). It begins with an introduction to supervised and unsupervised learning. It then discusses various generative models like autoencoders, variational autoencoders, and GANs. For GANs specifically, it explains that they involve two neural networks - a generator that produces new data instances, and a discriminator that evaluates them for authenticity. The generator and discriminator play a adversarial game, with the generator learning to produce more realistic samples to fool the discriminator while the discriminator gets better at identifying the generator's fakes. In the end, this competition helps the generator learn the true data distribution to a degree that it can produce highly realistic new samples.

1. Generative Adversarial Network
‫خالوئی‬ ‫محمد‬
‫امیرکبیر‬ ‫صنعتی‬ ‫دانشگاه‬ ‫رباتیک‬ ‫و‬ ‫مصنوعی‬ ‫هوش‬ ‫دکتری‬ ‫دانشجوی‬
‫آزمایشگاه‬ ‫عضو‬‫چندرسانه‬ ‫هوشمند‬ ‫های‬ ‫سیستم‬‫امیرکبیر‬ ‫صنعتی‬ ‫دانشگاه‬ ‫ای‬
‫شریف‬ ‫صنعتی‬ ‫دانشگاه‬ ‫داده‬ ‫کالن‬ ‫کارگروه‬ ‫ژرف‬ ‫یادگیری‬ ‫واحد‬ ‫مسئول‬
‫ارتباطی‬ ‫داالن‬:http://ceit.aut.ac.ir/~khalooeikhalooei@aut.ac.ir
Mohammad khalooeiGenerative Adversarial Network1
‫دیماه‬1396

2. ‫مطالب‬ ‫فهرست‬
•‫اول‬ ‫بخش‬
•‫مقدمه‬
•‫پارامتری‬ ‫غیر‬ ‫و‬ ‫پارامتری‬ ‫های‬‫مدل‬
•‫ماکزیمم‬ ‫گر‬‫تخمین‬ ‫بررسی‬likelihood
•‫داده؟‬ ‫تولید‬!
•AE
•VAE‫کلیات‬
•‫دوم‬ ‫بخش‬
•‫مقایسه‬ ‫صورت‬ ‫به‬ ‫برمفاهیم‬ ‫مروری‬
•‫های‬ ‫پژوهش‬ ‫آمار‬ ‫از‬ ‫دورنمایی‬GAN
•‫مختلف‬ ‫های‬ ‫اپلیکیشن‬GAN
Mohammad khalooeiGenerative Adversarial Network2

3. ‫مقدمه‬...
Mohammad khalooeiGenerative Adversarial Network
https://goo.gl/36eD3H
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4. ‫مقدمه‬...
•‫بانظارت‬ ‫یادگیری‬(Supervised learning)
Mohammad khalooeiGenerative Adversarial Network
‫گربه‬
f : y = f(x)
x : ‫داده‬
y : ‫برچسب‬
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Namjukim - 2017

5. ‫مقدمه‬...
•‫بانظارت‬ ‫یادگیری‬(Supervised learning)
Mohammad khalooeiGenerative Adversarial Network
f : y = f(x)
x : ‫داده‬
y : ‫برچسب‬
‫گربه‬:0.9
‫سگ‬:0.4
‫موش‬:0.6
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Namjukim - 2017

6. ‫مقدمه‬...
•‫بانظارت‬ ‫یادگیری‬(Supervised learning)
Mohammad khalooeiGenerative Adversarial Network
f : y = f(x)
x : ‫داده‬
y : ‫برچسب‬
‫گربه‬:0.9
‫سگ‬:0.4
‫موش‬:0.6
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Namjukim - 2017

7. ‫مقدمه‬...
•‫بانظارت‬ ‫یادگیری‬(Supervised learning)
Mohammad khalooeiGenerative Adversarial Network
f : y = f(x)
x : ‫داده‬
y : ‫برچسب‬
‫گربه‬:0.9
‫سگ‬:0.4
‫موش‬:0.6
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Namjukim - 2017

8. ‫مقدمه‬...
•‫نظارت‬ ‫بدون‬ ‫یادگیری‬(UnSupervised learning)
Mohammad khalooeiGenerative Adversarial Network
f : z = f(x)
x : ‫داده‬
z : latent
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Namjukim - 2017

9. ‫مقدمه‬...
•‫یادگیری‬‫بدون‬‫نظارت‬(UnSupervised learning)
Mohammad khalooeiGenerative Adversarial Network
f : z = f(x)
x : ‫داده‬
z : latent
‫شباهت‬ ‫معیار‬...
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10. ‫بر‬ ‫مبتنی‬ ‫های‬‫پژوهش‬ ‫روندگسترش‬GAN
Mohammad khalooeiGenerative Adversarial Network
https://deephunt.in/the-gan-zoo-79597dc8c347
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11. ‫مولد‬ ‫های‬‫مدل‬
Mohammad khalooeiGenerative Adversarial Network
G : x = g(z)
x : ‫داده‬
z : latent
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Namjukim - 2017

12. ‫مولد‬ ‫های‬‫مدل‬
Mohammad khalooeiGenerative Adversarial Network
G : x = g(z)
x : ‫داده‬
z : latent
‫های‬‫مدل‬‫مولد‬ ‫نظارت‬ ‫بدون‬
x = g(z) z = f(x)
P(x|z) P(z|x)
Decoder ( Generator ) Encoder
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Namjukim - 2017

13. ‫مولد‬ ‫های‬‫مدل‬
•‫توزیع‬ ‫تخمین‬ ‫بر‬ ‫مبتنی‬
•‫نمونه‬ ‫تولید‬ ‫بر‬ ‫مبتنی‬
Mohammad khalooeiGenerative Adversarial Network
‫تولیدی‬ ‫های‬‫نمونه‬‫آموزشی‬ ‫های‬‫نمونه‬Goodfellow 2016
https://arxiv.org/pdf/1701.00160.pdf
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14. ‫داده؟‬ ‫و‬ ‫یادگیری‬ ‫فضای‬!
Mohammad khalooeiGenerative Adversarial Network
‫ای‬‫داده‬ ‫فضای‬
Data space
‫نهان‬ ‫فضای‬
Latent space
https://goo.gl/XZVmCt
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15. ‫خودرمزکننده‬(AutoEncoder)
Mohammad khalooeiGenerative Adversarial Network
Fei-Fei Li Stanford 2017
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16. ‫خودرمزکننده‬(AutoEncoder)
Mohammad khalooeiGenerative Adversarial Network16
Namjukim - 2017

17. ‫خودرمزکننده‬(AutoEncoder)
Mohammad khalooeiGenerative Adversarial Network17
Namjukim - 2017

18. ‫خودرمزکننده‬(AutoEncoder)
Mohammad khalooeiGenerative Adversarial Network
Fei-Fei Li Stanford 2017
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19. VAE (Variational Autoencoders)
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Namjukim - 2017

20. VAE (Variational Autoencoders)
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Namjukim - 2017

21. VAE (Variational Autoencoders)
Mohammad khalooeiGenerative Adversarial Network
https://goo.gl/Jd3sQb
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22. VAE (Variational Autoencoders)
Mohammad khalooeiGenerative Adversarial Network
https://goo.gl/Jd3sQb
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23. VAE (Variational Autoencoders)
Mohammad khalooeiGenerative Adversarial Network
Fei-Fei Li Stanford 2017
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24. Generative Adversarial Network
Mohammad khalooeiGenerative Adversarial Network
Fei-Fei Li Stanford 2017
• Ian Goodfellow et al, “Generative Adversarial Networks”, 2014.
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25. Generative Adversarial Network
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Namjukim - 2017

26. Generative Adversarial Network
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Namjukim - 2017

27. Generative Adversarial Network
Mohammad khalooeiGenerative Adversarial Network
https://goo.gl/8AH4bj
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28. Generative Adversarial Network
Mohammad khalooeiGenerative Adversarial Network
https://blog.openai.com/generative-models/
VAE
learning to generate images (log time)
GAN
learning to generate images (linear time)
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29. Generative Adversarial Network
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Goodfellow et al. 2014
https://arxiv.org/pdf/1406.2661

30. Generative Adversarial Network
Mohammad khalooeiGenerative Adversarial Network
Goodfellow et al. 2014
https://arxiv.org/pdf/1406.2661
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31. Generative Adversarial Network
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32. Generative Adversarial Network
Mohammad khalooeiGenerative Adversarial Network
Fei-Fei Li Stanford 2017
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33. Generative Adversarial Network
Mohammad khalooeiGenerative Adversarial Network
Fei-Fei Li Stanford 2017
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34. Generative Adversarial Network
Mohammad khalooeiGenerative Adversarial Network34
Namjukim - 2017

35. Generative Adversarial Network
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Namjukim - 2017

36. Generative Adversarial Network
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37. Thank you!
Mohammad KHalooei
PhD student at Amirkabir University of Technology- Tehran Polytechnic
Laboratory of Intelligence and multimedia processing (limp.aut.ac.ir)
Big data work group at Sharif University of Technology (bigdataworkgroup.ir)
http://ceit.aut.ac.ir/~khalooei
khalooei@aut.ac.ir
Mohammad khalooeiGenerative Adversarial Network37
Dec 2017