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Generative Adversarial
Networks
Ian Goodfellow et al, “Generative Adversarial Networks”, 2014
kakao
sophie.24
2019.01.14
Referenced Slides
Namju Kim. https://www.slideshare.net/ssuser77ee21/generative-adversarial-networks-70896091

Yunjey Choi. https://www.slideshare.net/NaverEngineering/1-gangenerative-adversarial-network

Jaejun Yoo. https://www.slideshare.net/thinkingfactory/pr12-intro-to-gans-jaejun-yoo
!2
What is GAN?
!3
Generative Adversarial Networks
What is GAN?
!4
Generative Adversarial Network
Generating
• The generative model learns the distribution of training data. 

•
!5
Generating
• The generative model learns the distribution of training data. 

•
!6
Generating
• The generative model learns the distribution of training data. 

•
!7
Generating
• The generative model learns the distribution of training data. 

•
!8
Generating
• The generative model learns the distribution of training data. 

•
!9
Generating
• The generative model learns the distribution of training data. 

•
!10
Objective Function of GAN
!11
Objective Function of GAN
!12
Objective Function of GAN
!13
Training GAN
!14
Gradient descent
* Algorithm adopted from Generative Adversarial Nets, Ian Goodfellow et al. 2014

* k is hyperparameter, k=1 is used in this paper
Training GAN
• Training Tip
!15
Training GAN
!16
• Training Tip
Why does GAN work?
!17
Why does GAN work?
!18
Why does GAN work?
!19
Kullback–Leibler divergence, KLD
Jensen–Shannon divergence
Why does GAN work?
!20
Kullback–Leibler divergence, KLD
Jensen–Shannon divergence

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Generative Adversarial Networks