1시간만에 GAN(Generative Adversarial Network) 완전 정복하기

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Introduction
Generative
Adversarial Networks
Yunjey Choi
Korea University
DAVIAN LAB

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Speaker Introduction Introduction
B.S. in Computer Science & Engineering at Korea University
M.S. Student in Computer Science & Engineering at Korea University (Current)
Interest: Deep Learning,TensorFlow, PyTorch
GitHub Link: https://github.com/yunjey

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Referenced Slides Introduction
• Namju Kim. Generative Adversarial Networks (GAN)
https://www.slideshare.net/ssuser77ee21/generative-adversarial-networks-70896091
• Taehoon Kim. 지적 대화를 위한 깊고 넓은 딥러닝
https://www.slideshare.net/carpedm20/ss-63116251

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Branches of ML Introduction
Semi-supervised
Learning Unsupervised
Learning
Supervised
Learning
Reinforcement
Learning
Machine Learning
No labeled data
No feedback
“find hidden structure”
Labeled data
Direct feedback
No labeled data
Delayed feedback
Reward signal

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Supervised Learning Introduction
Discriminative
Model woman
The discriminative model learns how to classify input to its class.
man
(1)
(1)
(2)
(2)
Input image
(64x64x3)

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Unsupervised Learning Introduction
Generative
Model
Latent
code
The generative model learns the distribution of training data.
(100)
Image
(64x64x3)

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Generative Model
𝑋 1 2 3 4 5 6
𝑃 𝑋
1
6
1
6
1
6
0
6
1
6
2
6
1 2 3 4 5 6
1
6
2
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𝑝 𝑥
𝑥
Probability Distribution Introduction
Random variable
Probability Basics (Review)
Probability mass function

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Generative Model
What if 𝑥 is actual images in the training data?
At this point, 𝑥 can be represented as a (for example) 64x64x3 dimensional vector.

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Generative Model
𝑥
𝑝 𝑑𝑎𝑡𝑎(𝑥)
There is a 𝑝 𝑑𝑎𝑡𝑎(𝑥) that represents the distribution of actual images.
Probability density function

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Generative Model
𝑥
Let’s take an example with human face image dataset.
Our dataset may contain few images of men with glasses.
𝑥1 𝑥2 𝑥3 𝑥4
𝑥1 is a 64x64x3 high dimensional vector
representing a man with glasses.
The probability
density value is low

The probability
density value is high
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Generative Model
𝑥
Our dataset may contain many images of women with black hair.
𝑥1 𝑥2 𝑥3 𝑥4
representing a woman with black hair.

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Generative Model
𝑥
Our dataset may contain very many images of women with blonde hair.
𝑥1 𝑥2 𝑥3 𝑥4
representing a woman with blonde hair.
The probability
density value is
very high

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Generative Model
𝑥
Our dataset may not contain these strange images.
𝑥1 𝑥2 𝑥3 𝑥4
𝑥4 is an 64x64x3 high dimensional vector
representing very strange images.
The probability
density value is
almost 0

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Generative Model
𝑥
The goal of the generative model is to find a 𝑝 𝑚𝑜𝑑𝑒𝑙(𝑥) that
approximates 𝑝 𝑑𝑎𝑡𝑎(𝑥) well.
𝑥1 𝑥2 𝑥3 𝑥4
𝑝 𝑚𝑜𝑑𝑒𝑙(𝑥)
𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 𝑜𝑓 𝑎𝑐𝑡𝑢𝑎𝑙 𝑖𝑚𝑎𝑔𝑒𝑠
𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 𝑜𝑓 𝑖𝑚𝑎𝑔𝑒𝑠 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 𝑏𝑦 𝑡ℎ𝑒 𝑚𝑜𝑑𝑒𝑙

Generative Adversarial
Networks
02

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Intuition in GAN GANs
G(z)
DGz D(G(z))
D D(x)
x
Fake image
Real image
The probability of that
x came from the real data
(0~1)Discriminator
Generator
Latent Code
May be high
May be low
Training with real images
Training with fake images

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G(z)
DGz D(G(z))
D D(x)
x
Real image
(64x64x3)
This value should
be close to 1.Discriminator
(Neural Network)
The discriminator should classify
a real image as real.

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G(z)
Dz D(G(z))
D D(x)
x
Fake image generated by the generator
(64x64x3)
Generator
This value should
be close to 0.
The discriminator should classify
a fake image as fake.

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G(z)
DGz D(G(z))
D D(x)
x
Generated image
(64x64x3)
Generator
(Neural Network)
Latent Code
(100)
This value should
be close to 1.
The generator should create an image
that is indistinguishable from real to
deceive the discriminator

𝑚𝑖𝑛 𝑚𝑎𝑥 𝑉 𝐷, 𝐺 = 𝐸 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝐸𝑧~𝑝 𝑧(𝑧) log(1 − 𝐷(𝐺(𝑧)))
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Objective Function of GAN GANs
G(z)
DGz D(G(z))
D D(x)
x
𝐺 𝐷
Maximum when 𝐷(𝑥) = 1 Maximum when 𝐷(𝐺(𝑧)) = 0
Sample 𝑥 from real data distribution Sample latent code 𝑧 from Gaussian distribution
Train D to classify fake images as fake
Train D to classify real images as real
𝐷 should maximize 𝑉(𝐷, 𝐺)
Objective function

𝑚𝑖𝑛 𝑚𝑎𝑥 𝑉 𝐷, 𝐺 = 𝐸 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝐸𝑧~𝑝 𝑧(𝑧) log(1 − 𝐷(𝐺(𝑧)))
𝐺 is independent of this part
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Objective Function of GAN GANs
𝐺 should minimize 𝑉(𝐷, 𝐺) Minimum when 𝐷(𝐺(𝑧)) = 1
G(z)
DGz D(G(z))
D D(x)
x
Train G to deceive D
𝐺 𝐷
Objective function

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PyTorch Implementation GANs
G(z) DGz D(G(z))
D D(x)x

Define the discriminator
input size: 784
hidden size: 128
output size: 1
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G(z) DGz D(G(z))
D D(x)x
Discriminator
Assume x is MNIST (784 dimension)
Output probability(1 dimension)

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G(z) DGz D(G(z))
D D(x)x
Generator
Define the generator
input size: 100
hidden size: 128
output size: 784
Latent code (100 dimension) Generated image (784 dimension)

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G(z) DGz D(G(z))
D D(x)x
Binary Cross Entropy Loss (ℎ(𝑥), 𝑦)
− 𝑦 log ℎ 𝑥 − (1 − 𝑦) log(1 − ℎ(𝑥))

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G(z) DGz D(G(z))
D D(x)x
Optimizer for D and G

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G(z) DGz D(G(z))
D D(x)x
x is a tensor of shape (batch_size, 784).
z is a tensor of shape (batch_size, 100).

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G(z) DGz D(G(z))
D D(x)x
D(x) gets closer to 1.
D(G(z)) gets closer to 0
Forward, Backward and Gradient Descent
Train the discriminator
with real images
Train the discriminator
with fake images

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G(z) DGz D(G(z))
D D(x)x
Forward, Backward and Gradient Descent
Train the generator
to deceive the discriminator

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G(z) DGz D(G(z))
D D(x)x
The complete code can be found here
https://github.com/yunjey/pytorch-tutorial

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Objective Function of Generator
𝐺
Objective function of G
Non-Saturating Game GANs
Images created by the generator
at the beginning of training
𝑦 = log(1 − 𝑥)
The gradient is relatively small at D(G(z))=0
At the beginning of training, the discriminator can clearly classify the
generated image as fake because the quality of the image is very low.
This means that D(G(z)) is almost zero at early stages of training.
𝑚𝑖𝑛 𝐸𝑧~𝑝 𝑧(𝑧) log(1 − 𝐷(𝐺 𝑧 )

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Solution for Poor Gradient
𝐺
𝑚𝑎𝑥 𝐸𝑧~𝑝 𝑧(𝑧) log 𝐷(𝐺 𝑧 )
𝐺
𝑚𝑖𝑛 𝐸𝑧~𝑝 𝑧(𝑧) log(1 − 𝐷(𝐺 𝑧 )
Non-Saturating Game GANs
𝑦 = log(𝑥)
The gradient is very large at D(G(z))=0
Use binary cross entropy loss function with fake label (1)
𝑚𝑖𝑛 𝐸𝑧~𝑝 𝑧(𝑧)[−𝑦 log 𝐷(𝐺 𝑧 ) − 1 − 𝑦 log(1 − 𝐷(𝐺 𝑧 )]
𝑚𝑖𝑛 𝐸𝑧~𝑝 𝑧(𝑧)[− log 𝐷 𝐺 𝑧 ]
𝐺
𝐺
𝑦 = 1
Modification (heuristically motivated)
• Practical Usage

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Theory in GAN GANs
𝐸 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝐸𝑧~𝑝 𝑧(𝑧) log(1 − 𝐷(𝐺 𝑧 )
𝑚𝑖𝑛 𝐽𝑆𝐷(𝑝 𝑑𝑎𝑡𝑎||𝑝 𝑔)
𝐺, 𝐷
𝑚𝑖𝑛 𝑚𝑎𝑥 𝑉 𝐷, 𝐺
𝐺 𝐷
𝑥
𝑝 𝑔(𝑥)
𝐽𝑆𝐷(𝑃||𝑄) =
1
2
𝐾𝐿(𝑃| 𝑀 +
1
2
𝐾𝐿(𝑄| 𝑀
𝑤ℎ𝑒𝑟𝑒 𝑀 =
1
2
(𝑃 + 𝑄) KL Divergence
• Why does GANs work?
same
Please see Appendix for details
Because it actually minimizes the distance between the real data distribution and the model distribution.
Objective function of GANs Jenson-Shannon divergence

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DCGAN Variants of
GAN
Radford et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2015
• Deep Convolutional GAN(DCGAN), 2015
The authors present a model that is still highly preferred.

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DCGAN Variants of
GAN
DGz D(G(z))
D D(x)
x
Use convolution, Leaky ReLU
Use deconvolution, ReLU
• No pooling layer (Instead strided convolution)
• Use batch normalization
• Adam optimizer(lr=0.0002, beta1=0.5, beta2=0.999)

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DCGAN Variants of
GAN
Radford et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, 2015
• Latent vector arithmetic

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LSGAN Variants of
GAN
Xudong Mao et al. Least Squares Generative Adversarial Networks, 2016
• Least Squares GAN (LSGAN)
Proposed a GAN model that adopts the least squares loss function for the discriminator.

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LSGAN Variants of
GAN
Vanilla GAN LSGAN
Remove sigmoid
non-linearity
in last layer

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LSGAN Variants of
GAN
Vanilla GAN LSGAN
Generator is the
same as original

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LSGAN Variants of
GAN
Vanilla GAN LSGAN
Replace cross entropy loss
to least squares loss (L2)
D(x) gets closer to 1
(same as original)

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LSGAN Variants of
GAN
Vanilla GAN LSGAN
Replace cross entropy loss
to least squares loss (L2)
(same as original)

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LSGAN Variants of
GAN
• Results (LSUN dataset)
Xudong Mao et al. Least Squares Generative Adversarial Networks, 2016

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LSGAN Variants of
GAN
• Results (CelebA)

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SGAN Variants of
GAN
D
G D
one-hot vector representing 2
Real image
latent vector z
fake image
(1) FC layer with softmax
• Semi-Supervised GAN
11 dimension
(10 classes + fake)
(1)
(1)
one-hot vector representing a fake label
Augustus Odena et al. Semi-Supervised Learning with Generative Adversarial Netwoks, 2016

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SGAN Variants of
GAN
• Results (Game Character)
1 2 3 4 5
The generator can create an character image that takes a certain pose.
one-hot vectors
representing class labels
The code will be available soon: https://github.com/yunjey

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ACGAN Variants of
GAN
Augustus Odena et al. Conditional Image Synthesis with Auxilary Classifier, 2016
• Auxiliary Classifier GAN(ACGAN), 2016
Proposed a new method for improved training of GANs using class labels.

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ACGAN Variants of
GAN
real or fake?
D
G
real or fake?
D
Real image
latent vector z
fake image
(1) FC layer with sigmoid
(2) FC layer with softmax
(1)
(2)
Discriminator
(multi-task learning)
(1)
(2)
• How does it work?

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CycleGAN Extensions
• CycleGAN: Unpaired Image-to-Image Translation
presents a GAN model that transfer an image from a source domain A to a target
domain B in the absence of paired examples.
Jun-Yan Zhu et al. Unpaired Image-to-Image Translation using Cycle Consistent Adversarial Networks, 2017

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CycleGAN
real or fake ? DB
GAB
Real Image in domain A Fake Image in domain B
Real Image in domain B
Discriminator for domain B
The generator GAB should generates a horse
from the zebra to deceive the discriminator DB.
Extensions

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CycleGAN
DB
GAB
Discriminator for domain B
GBA
Reconstructed Image
L2 Loss
GBA generates a reconstructed image of domain A.
This makes the shape to be maintained
when GAB generates a horse image from the zebra.real or fake ?
Real Image in domain A
Real Image in domain B
Fake Image in domain B
Extensions

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CycleGAN Extensions
• Results
Jun-Yan Zhu et al. Unpaired Image-to-Image Translation using Cycle Consistent Adversarial Networks, 2017

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CycleGAN Extensions
• Results
MNIST-to-SVHNSVHN-to-MNIST
Odd columns contain real images and even columns contain generated images.
https://github.com/yunjey/mnist-svhn-transfer

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StackGAN
Han Zhang et al. StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks, 2016
• StackGAN:Text to Photo-realistic Image Synthesis
Extensions

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StackGAN
Fake image
(128x128)
Real image
(128x128)
D
G
Z
(100)
‘real’
or
‘fake’
Generates a128x128 image from scratch (not guarantee good result)
Discriminator for 128x128 image
• Generating 128x128 from scratch
Extensions

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StackGAN Extensions
D1 D2
G1 G2
Fake image
(64x64)
Real image
(64x64)
Fake image
(128x128)
Real image
(128x128)
‘real’
or
‘fake’
Generates a 64x64 image
Upscales a 64x64 image to 128x128 (Easier than generating from scratch).
Discriminator for 64x64 image Discriminator for 128x128 image
• Generating 128x128 from 64x64
‘real’
or
‘fake’
Z
(100)

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Latest Work
Jing Liao et al. Visual Attribute Transfer through Deep Image Analogy, 2017
Extensions
• Visual Attribute Transfer

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Latest Work
Richard Zhang et al. Real-Time User-Guided Image Colorization with Learned Deep Prioirs, 2017
Extensions
• User-Interactive Image Colorization

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Convergence Measure
• Boundary Equilibrium GAN (BEGAN)
Extensions
David Berthelot et al. BEGAN: Boundary Equilibrium Generative Adversarial Networks, 2017

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Convergence Measure
• Reconstruction Loss
Extensions
Sitao Xiang. On the effect of Batch Normalization and Weight Normalization in Generative Adversarial Network, 2017
Gz
G(z) x
Test images

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Better Upsampling
• Deconvolution Checkboard Artifacts
Extensions
http://distill.pub/2016/deconv-checkerboard/

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Better Upsampling
• Deconvolution vs Resize-Convolution
Extensions
http://distill.pub/2016/deconv-checkerboard/

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GAN in Supervised Learning
• Machine Translation (Seq2Seq)
Extensions
A B C D
Y
X Y Z<start>
Z <end>X
Should ‘ABCD’ be translated to ‘XYZ’?
Tackling the supervised learning

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GAN in Supervised Learning
• Machine Translation (GANs)
Extensions
D
G
D
Training with real sentences
Training with fake sentences
A
(English)
B
(Korean)
Does A and B have the same meaning?
The discriminator should say ‘yes’.
Does A and B have the same meaning?
The discriminator should say ‘no’.
A
(English)
B
(Fake Korean)
Lijun Wu et al. Adversarial Neural Machine Translation, 2017
The generator should generate B
that has the same meaning as
A to deceive the discriminator.

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𝑚𝑖𝑛 𝑚𝑎𝑥 𝑉 𝐷, 𝐺 = 𝐸 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝐸𝑧~𝑝 𝑧(𝑧) log(1 − 𝐷(𝐺 𝑧 )
𝐺 𝐷
Theory in GAN GANs
𝑂𝑏𝑗𝑒𝑐𝑡𝑖𝑣𝑒 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝐺𝐴𝑁𝑠 𝑟𝑒𝑎𝑙 𝑑𝑎𝑡𝑎 𝑑𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 𝐺𝑎𝑢𝑠𝑠𝑖𝑎𝑛 𝑑𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛
ℎ𝑖𝑔ℎ 𝑑𝑖𝑚𝑒𝑛𝑠𝑖𝑜𝑛𝑎𝑙 𝑣𝑒𝑐𝑡𝑜𝑟 (𝑒. 𝑔. 64 × 64) l𝑜𝑤 𝑑𝑖𝑚𝑒𝑛𝑠𝑖𝑜𝑛𝑎𝑙 𝑣𝑒𝑐𝑡𝑜𝑟 (𝑒. 𝑔. 100)

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𝐺 𝐷
Theory in GAN GANs
𝐷∗(𝑥) = 𝑎𝑟𝑔 𝑚𝑎𝑥 𝑉 𝐷 = 𝐸 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝐸𝑧~𝑝 𝑧(𝑧) log(1 − 𝐷(𝐺 𝑧 )
𝐹𝑖𝑥 𝐺 𝑡𝑜 𝑚𝑎𝑘𝑒 𝑖𝑡 𝑡𝑜 𝑎 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝐷
𝑂𝑝𝑡𝑖𝑚𝑎𝑙 𝐷
𝐷
𝐺𝑒𝑡 𝐷 𝑤ℎ𝑒𝑛 𝑉 𝐷 𝑖𝑠 𝑚𝑎𝑥𝑖𝑚𝑢𝑚

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𝐺 𝐷
Theory in GAN GANs
= 𝐸 𝑥~𝑝 𝑑𝑎𝑡𝑎(𝑥) log 𝐷 𝑥 + 𝐸 𝑥~𝑝 𝑔(𝑥) log(1 − 𝐷(𝑥))
𝑠𝑎𝑚𝑝𝑙𝑖𝑛𝑔 𝑥 𝑓𝑟𝑜𝑚 𝑝 𝑔
𝑖𝑛𝑠𝑡𝑒𝑎𝑑 𝑜𝑓 𝑠𝑎𝑚𝑝𝑙𝑖𝑛𝑔 𝑧 𝑓𝑟𝑜𝑚 𝑝𝑧
𝑣𝑒𝑐𝑡𝑜𝑟 𝑜𝑓 64 × 64 𝑑𝑖𝑚𝑒𝑛𝑠𝑖𝑜𝑛
𝑣𝑒𝑐𝑡𝑜𝑟 𝑜𝑓 100 𝑑𝑖𝑚𝑒𝑛𝑠𝑖𝑜𝑛
𝑚𝑜𝑑𝑒𝑙 𝐺 𝑑𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 𝑓𝑜𝑟 ℎ𝑖𝑔ℎ 𝑑𝑖𝑚𝑒𝑛𝑠𝑖𝑜𝑛𝑎𝑙 𝑣𝑒𝑐𝑡𝑜𝑟 (𝑒. 𝑔. 64 × 64)
𝐷

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𝐺 𝐷
Theory in GAN GANs
= න 𝑝 𝑑𝑎𝑡𝑎 𝑥 log 𝐷(𝑥) 𝑑𝑥 + න 𝑝 𝑔 𝑥 log 1 − 𝐷 𝑥 𝑑𝑥
𝑥 𝑥
= න 𝑝 𝑥 𝑓 𝑥 𝑑𝑥𝐸 𝑥~𝑝(𝑥) 𝑓(𝑥)
𝐷𝑒𝑓𝑖𝑛𝑖𝑡𝑖𝑜𝑛 𝑜𝑓 𝐸𝑥𝑝𝑒𝑐𝑡𝑎𝑡𝑖𝑜𝑛
𝑥
𝐼𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑒 𝑓𝑜𝑟 𝑎𝑙𝑙 𝑝𝑜𝑠𝑠𝑖𝑏𝑙𝑒 𝑥
𝐷

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𝐺 𝐷
Theory in GAN GANs
= න 𝑝 𝑑𝑎𝑡𝑎 𝑥 log 𝐷 𝑥 + 𝑝 𝑔 𝑥 log 1 − 𝐷 𝑥 𝑑𝑥
𝑥
𝑥 𝑥
𝑥
𝐵𝑎𝑠𝑖𝑐 𝑝𝑟𝑜𝑝𝑒𝑟𝑡𝑦 𝑜𝑓 𝐼𝑛𝑡𝑒𝑔𝑟𝑎𝑙
𝐷

75
𝐺 𝐷
Theory in GAN GANs
= න 𝑝 𝑑𝑎𝑡𝑎 𝑥 log 𝐷 𝑥 + 𝑝 𝑔 𝑥 log 1 − 𝐷 𝑥 𝑑𝑥
𝑥
𝑥 𝑥
𝑥
𝐵𝑎𝑠𝑖𝑐 𝑝𝑟𝑜𝑝𝑒𝑟𝑡𝑦 𝑜𝑓 𝐼𝑛𝑡𝑒𝑔𝑟𝑎𝑙
𝑁𝑜𝑤 𝑤𝑒 𝑛𝑒𝑒𝑑 𝑡𝑜 𝑓𝑖𝑛𝑑 𝐷 𝑥 𝑤ℎ𝑖𝑐ℎ 𝑚𝑎𝑘𝑒𝑠 𝑡ℎ𝑒 𝑓𝑢𝑛𝑡𝑖𝑜𝑛 𝑖𝑛𝑠𝑖𝑑𝑒 𝑖𝑛𝑡𝑒𝑔𝑟𝑎𝑙 𝑚𝑎𝑥𝑖𝑚𝑢𝑚.
𝐷

76
Theoretical Results
= 𝑎𝑟𝑔 𝑚𝑎𝑥 𝑝 𝑑𝑎𝑡𝑎 𝑥 log 𝐷 𝑥 + 𝑝 𝑔 𝑥 log 1 − 𝐷 𝑥
Theory in GAN GANs
𝐷∗(𝑥) = 𝑎𝑟𝑔 𝑚𝑎𝑥 𝑉 𝐷
𝐷
𝐷
𝑇ℎ𝑒 𝑓𝑢𝑛𝑡𝑖𝑜𝑛 𝑖𝑛𝑠𝑖𝑑𝑒 𝑖𝑛𝑡𝑒𝑔𝑟𝑎𝑙

77
Theoretical Results
Theory in GAN GANs
𝐷
𝐷
𝑎 log 𝑦 + 𝑏 log 1 − 𝑦
𝑆𝑢𝑏𝑠𝑡𝑖𝑡𝑢𝑡𝑒 𝑎 = 𝑝 𝑑𝑎𝑡𝑎 𝑥 , 𝑦 = 𝐷 𝑥 , 𝑏 = 𝑝 𝑔 𝑥

78
Theoretical Results
Theory in GAN GANs
𝐷
𝐷
𝑎
𝑦
+
−𝑏
1 − 𝑦
=
𝑎 − (𝑎 + 𝑏)𝑦
𝑦(1 − 𝑦)
𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡𝑖𝑎𝑡𝑒 𝑤𝑖𝑡ℎ 𝑟𝑒𝑠𝑝𝑒𝑐𝑡 𝑡𝑜 𝐷 𝑥 𝑢𝑠𝑖𝑛𝑔
𝑁𝑜𝑡𝑒 𝑡ℎ𝑎𝑡 𝐷 𝑥 𝑐𝑎𝑛 𝑛𝑜𝑡 𝑎𝑓𝑓𝑒𝑐𝑡 𝑡𝑜 𝑝 𝑑𝑎𝑡𝑎 𝑥 𝑎𝑛𝑑 𝑝 𝑔 𝑥 .
𝑑
𝑑𝑥
log 𝑓(𝑥) =
𝑓′
(𝑥)
𝑓(𝑥)

79
Theoretical Results
Theory in GAN GANs
𝐷
𝐷
𝑎
𝑦
+
−𝑏
1 − 𝑦
𝑎 − (𝑎 + 𝑏)𝑦
𝑦(1 − 𝑦)
= 0
𝑦 =
𝑎
𝑎 + 𝑏
=
𝑎 − (𝑎 + 𝑏)𝑦
𝑦(1 − 𝑦)
𝐼𝑡 ℎ𝑎𝑠 𝑎 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑣𝑎𝑙𝑢𝑒 𝑤ℎ𝑒𝑛
𝑁𝑜𝑡𝑒 𝑡ℎ𝑎𝑡 𝑡ℎ𝑒 𝑙𝑜𝑐𝑎𝑙 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑖𝑠 𝑡ℎ𝑒 𝑔𝑙𝑜𝑏𝑎𝑙 𝑚𝑎𝑥𝑖𝑚𝑢𝑚
𝑤ℎ𝑒𝑛 𝑡ℎ𝑒𝑟𝑒 𝑎𝑟𝑒 𝑛𝑜 𝑜𝑡ℎ𝑒𝑟 𝑙𝑜𝑐𝑎𝑙 𝑒𝑥𝑡𝑟𝑒𝑚𝑒𝑠.
𝑑
𝑑𝑥
log 𝑓(𝑥) =
𝑓′
(𝑥)
𝑓(𝑥)
𝐹𝑖𝑛𝑑 𝑡ℎ𝑒 𝑝𝑜𝑖𝑛𝑡 𝑤ℎ𝑒𝑟𝑒 𝑡ℎ𝑒 𝑑𝑒𝑟𝑖𝑣𝑎𝑡𝑖𝑣𝑒 𝑣𝑎𝑙𝑢𝑒 𝑖𝑠 0 𝑙𝑜𝑐𝑎𝑙 𝑒𝑥𝑡𝑟𝑒𝑚𝑒 .

80
Theoretical Results
Theory in GAN GANs
𝐷
𝐷
𝑎
𝑦
+
−𝑏
1 − 𝑦
𝑎 − (𝑎 + 𝑏)𝑦
𝑦(1 − 𝑦)
= 0
𝑦 =
𝑎
𝑎 + 𝑏
=
𝑎 − (𝑎 + 𝑏)𝑦
𝑦(1 − 𝑦)
𝐼𝑡 ℎ𝑎𝑠 𝑎 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑣𝑎𝑙𝑢𝑒 𝑤ℎ𝑒𝑛
𝑑
𝑑𝑥
log 𝑓(𝑥) =
𝑓′
(𝑥)
𝑓(𝑥)
𝐹𝑖𝑛𝑑 𝑡ℎ𝑒 𝑝𝑜𝑖𝑛𝑡 𝑤ℎ𝑒𝑟𝑒 𝑡ℎ𝑒 𝑑𝑒𝑟𝑖𝑣𝑎𝑡𝑖𝑣𝑒 𝑣𝑎𝑙𝑢𝑒 𝑖𝑠 0 𝑙𝑜𝑐𝑎𝑙 𝑒𝑥𝑡𝑟𝑒𝑚𝑒 .
𝐷∗ 𝑥 =
𝑝 𝑑𝑎𝑡𝑎(𝑥) + 𝑝 𝑔(𝑥)

81
Theory in GAN GANs
= 𝐸 𝑥~𝑝 𝑑𝑎𝑡𝑎
log 𝐷∗
𝑥 + 𝐸 𝑥~𝑝 𝑔
log(1 − 𝐷∗
(𝑥))
= න 𝑝 𝑑𝑎𝑡𝑎 𝑥 log
𝑑𝑥 + න 𝑝 𝑔 𝑥 log
𝑝 𝑔(𝑥)
𝑑𝑥
= −𝑙𝑜𝑔4 + 𝑙𝑜𝑔4 + න 𝑝 𝑑𝑎𝑡𝑎 𝑥 log
𝑝 𝑔(𝑥)
𝑑𝑥
= −𝑙𝑜𝑔4 + න 𝑝 𝑑𝑎𝑡𝑎 𝑥 log
2 ∙ 𝑝 𝑑𝑎𝑡𝑎(𝑥)
2 ∙ 𝑝 𝑔(𝑥)
𝑑𝑥
= −𝑙𝑜𝑔4 + 𝐾𝐿(𝑝 𝑑𝑎𝑡𝑎||
𝑝 𝑑𝑎𝑡𝑎 + 𝑝 𝑔
2
) + 𝐾𝐿(𝑝 𝑔||
2
)
= −𝑙𝑜𝑔4 + 2 ∙ 𝐽𝑆𝐷(𝑝 𝑑𝑎𝑡𝑎||𝑝 𝑔)
𝑥 𝑥
𝑥 𝑥
𝑥𝑥
𝑚𝑖𝑛 𝑚𝑎𝑥 𝑉 𝐷, 𝐺 =
𝐺 𝐷
𝑚𝑖𝑛 𝑉 𝐷∗, 𝐺
𝐺
𝑉 𝐷∗, 𝐺

82
Theory in GAN GANs
= 𝐸 𝑥~𝑝 𝑑𝑎𝑡𝑎
log 𝐷∗
𝑥 + 𝐸 𝑥~𝑝 𝑔
log(1 − 𝐷∗
(𝑥))
= න 𝑝 𝑑𝑎𝑡𝑎 𝑥 log
𝑝 𝑔(𝑥)
𝑑𝑥
= −𝑙𝑜𝑔4 + 𝑙𝑜𝑔4 + න 𝑝 𝑑𝑎𝑡𝑎 𝑥 log
𝑝 𝑔(𝑥)
𝑑𝑥
= −𝑙𝑜𝑔4 + න 𝑝 𝑑𝑎𝑡𝑎 𝑥 log
2 ∙ 𝑝 𝑑𝑎𝑡𝑎(𝑥)
2 ∙ 𝑝 𝑔(𝑥)
𝑑𝑥
= −𝑙𝑜𝑔4 + 𝐾𝐿(𝑝 𝑑𝑎𝑡𝑎||
2
) + 𝐾𝐿(𝑝 𝑔||
2
)
= −𝑙𝑜𝑔4 + 2 ∙ 𝐽𝑆𝐷(𝑝 𝑑𝑎𝑡𝑎||𝑝 𝑔)
𝑥 𝑥
𝑥 𝑥
𝑥𝑥
𝑚𝑖𝑛 𝑚𝑎𝑥 𝑉 𝐷, 𝐺 =
𝐺 𝐷
𝑚𝑖𝑛 𝑉 𝐷∗, 𝐺
𝐺
𝑉 𝐷∗, 𝐺
𝐺 𝑠ℎ𝑜𝑢𝑙𝑑 𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒
𝐺 𝑠ℎ𝑜𝑢𝑙𝑑 𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑒
𝑂𝑝𝑡𝑖𝑚𝑖𝑧𝑖𝑛𝑔 𝑉 𝐷, 𝐺 𝑖𝑠 𝑠𝑎𝑚𝑒 𝑎𝑠 𝑚𝑖𝑛𝑖𝑚𝑖𝑧𝑖𝑛𝑔 𝐽𝑆𝐷(𝑝 𝑑𝑎𝑡𝑎||𝑝 𝑔)
𝑂𝑝𝑡𝑖𝑚𝑎𝑙 𝐷

1시간만에 GAN(Generative Adversarial Network) 완전 정복하기

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