A generative adversarial network (GAN) is a class of machine learning systems. Two neural networks contest with each other in a zero-sum game framework [WikiPedia].
In this presentation, I try to cover the concepts of GAN and it's applications.
This presentations was presented by Mohammad Khalooei in WSS 2018 (Winter Seminar Series) at Sharif University of Technology.
Mattingly "AI & Prompt Design: Named Entity Recognition"
Generative Adversarial Network
1. Generative Adversarial Network
Presented by Mohammad Khalooei
PhD student of Amirkabir University of Technology (Tehran Polytecnic)
Under supervision of Prof. Mohammad Mehdi Homayounpour & Dr. Maryam Amirmazlaghani
Laboratory of Intelligence and Multimedia Processing (LIMP)
http://ceit.aut.ac.ir/~khalooei
khalooei [at] aut.ac.ir
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 1
2. Generative Adversarial Network
Presented by Mohammad Khalooei
PhD student of Amirkabir University of Technology (Tehran Polytecnic)
Under supervision of Prof. Mohammad Mehdi Homayounpour & Dr. Maryam Amirmazlaghani
Laboratory of Intelligence and Multimedia Processing (LIMP)
http://ceit.aut.ac.ir/~khalooei
khalooei [at] aut.ac.ir
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 2
3. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 3
GAN Zoo!
https://github.com/hindupuravinash/the-gan-zoo
4. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 4
Best paper statistic from CVPR 2018
Are GANs the new Deep?
http://jponttuset.cat/are-gans-the-new-deep/
5. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 5
Best paper statistic from CVPR 2018
The most important one, in my opinion, is adversarial
training (also called GAN for Generative Adversarial
Networks).
https://medium.com/syncedreview/cvpr-2018-kicks-off-best-papers-announced-d3361bcc6984
Yann LeCun
More than eight percent of CVPR 2018’s
accepted papers include “GANs”
in their titles,
doubling the frequency at CVPR 2017.
Google AI Research Scientist Jordi Pont-Tuset suggested
in his blog that Generative Adversarial Networks (GANs)
might catch up with deep learning someday. Jordi Pont-Tuset
6. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 6
A brief Applications of GAN :: overview on CVPR18 paper
• Perceptual Fidelity
• Data Augmentation
• Adversarial Attack
• Domain Adaptation
• Improved GAN
• Metric Learning
Categories:
7. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 7
GAN !
https://goo.gl/oCdBRj
https://goo.gl/ibYzBr
8. Supervised learning
• Find deterministic function f
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 8
Introduction
x : data
y : label
f : y = f(x)
9. Supervised learning
• Find deterministic function f
• Challenges:
- Image is high dimensional data
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 9
Introduction
x : data
y : label
f : y = f(x)
3×224×224
224 px
224 px
R
G
B
= 150528
10. Supervised learning
• Find deterministic function f
• Challenges:
- Image is high dimensional data
- Many variations
Viewpoint, illumination, deformation,
occlusion, background clutter,
intraclass variation
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 10
Introduction
x : data
y : label
f : y = f(x)
11. Supervised learning
• Find deterministic function f
• Challenges:
- Image is high dimensional data
- Many variations
Viewpoint, illumination, deformation,
occlusion, background clutter,
intraclass variation
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 11
Introduction
x : data
y : label
f : y = f(x)
All pixels change when the camera moves !
http://cs231n.stanford.edu/slides/2018/cs231n_2018_lecture02.pdf
12. Supervised learning
• Find deterministic function f
• Challenges:
- Image is high dimensional data
- Many variations
Viewpoint, illumination, deformation,
occlusion, background clutter,
intraclass variation
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 12
Introduction
x : data
y : label
f : y = f(x)
http://cs231n.stanford.edu/slides/2018/cs231n_2018_lecture02.pdf
13. Supervised learning
• Find deterministic function f
• Challenges:
- Image is high dimensional data
- Many variations
Viewpoint, illumination, deformation,
occlusion, background clutter,
intraclass variation
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 13
Introduction
x : data
y : label
f : y = f(x)
http://cs231n.stanford.edu/slides/2018/cs231n_2018_lecture02.pdf
14. Supervised learning
• Find deterministic function f
• Challenges:
- Image is high dimensional data
- Many variations
Viewpoint, illumination, deformation,
occlusion, background clutter,
intraclass variation
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 14
Introduction
x : data
y : label
f : y = f(x)
http://cs231n.stanford.edu/slides/2018/cs231n_2018_lecture02.pdf
15. Supervised learning
• Find deterministic function f
• Challenges:
- Image is high dimensional data
- Many variations
Viewpoint, illumination, deformation,
occlusion, background clutter,
intraclass variation
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 15
Introduction
x : data
y : label
f : y = f(x)
http://cs231n.stanford.edu/slides/2018/cs231n_2018_lecture02.pdf
16. Supervised learning
• Find deterministic function f
• Challenges:
- Image is high dimensional data
- Many variations
Viewpoint, illumination, deformation,
occlusion, background clutter,
intraclass variation
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 16
Introduction
x : data
y : label
f : y = f(x)
http://cs231n.stanford.edu/slides/2018/cs231n_2018_lecture02.pdf
17. Supervised learning
• Find deterministic function f
• Challenges:
- Image is high dimensional data
- Many variations
Viewpoint, illumination, deformation,
occlusion, background clutter,
intraclass variation
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 17
Introduction
x : data
y : label
f : y = f(x)
• Solution:
- Feature Vector
3×224×224
224 px
224 px
R
G
B
= 150528 2048
Feature extractor
18. Supervised learning
• Find deterministic function f
• Challenges:
- Image is high dimensional data
- Many variations
Viewpoint, illumination, deformation,
occlusion, background clutter,
intraclass variation
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 18
Introduction
x : data
y : label
f : y = f(x)
• Solution:
- Feature Vector :: Synonyms
Latent Vector
Hidden Vector
Unobservable Vector
Feature
Representation
20. Supervised learning
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 20
Introduction
f : y = f(x)
Good features:
Less redundancy
Similar features for similar data
High fidelity
Good Bad
21. Supervised learning
• More flexible solution
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 21
Introduction
x : data
y : label
f : y = f(x)
Cat
22. Supervised learning
• More flexible solution
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 22
Introduction
x : data
y : label
f : y = f(x)
0.87 Cat
0.22 Dog
0.01 Cake
23. Supervised learning
• More flexible solution
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 23
Introduction
x : data
y : label
f : y = f(x)
0.87 Cat
0.22 Dog
0.01 Cake
24. UnSupervised learning
• Find deterministic function f
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 24
Introduction
x : data
z : latent
f : z = f(x)
Similaritymeasure
25. UnSupervised learning
• Find deterministic function f
• More challenging than supervised learning !
• No label or curriculum → self learning
• Some NN solutions :
• Boltzmann machine
• Auto-encoder or Variational Inference
• Generative Adversarial Network
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 25
Introduction
x : data
z : latent
f : z = f(x)
26. Generative model
• Find generation function g
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 26
Introduction
x : data
z : latent
g : x = g(z)
27. Generative model
• Find generation function g
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 27
Introduction
x : data
z : latent
g : x = g(z)
UnSupervised learning
• Find deterministic function f
x : data
z : latent
f : z = f(x)
VS.
28. Generative model
• Find generation function g
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 28
Introduction
x : data
z : latent
g : x = g(z)
UnSupervised learning
• Find deterministic function f
x : data
z : latent
f : z = f(x)
VS.
P(z|x)
P(x|z)
29. Generative model
• Find generation function g
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 29
Introduction
x : data
z : latent
g : x = g(z)
UnSupervised learning
• Find deterministic function f
x : data
z : latent
f : z = f(x)
VS.
P(z|x)
P(x|z)
Encod
er
Decoder
(Generator)
30. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 30
Generative Modeling
Sample GeneratorTraining Data
Training Data Density function
Sample Generation
Density Estimation
32. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 32
AutoEncoder
• Family of AE:
• Stack AutoEncoder (SAE) :: Use data itself as label / z = f(x), x=g(z) → x = g(f(x))
• Denoising autoencoder (DAE) :: Add random noise to input data
• Variational autoencoder (VAE) :: Generative Model + Stacked Autoencoder
• …
33. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 33
AutoEncoder
• Family of AE:
• Stack AutoEncoder (SAE) :: Use data itself as label / z = f(x), x=g(z) → x = g(f(x))
• Denoising autoencoder (DAE) :: Add random noise to input data
• Variational autoencoder (VAE) :: Generative Model + Stacked Autoencoder
• …
Sample Code:
https://github.com/buriburisuri/sugartensor/blob/master/sugart
ensor/example/mnist_sae.py
34. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 34
AutoEncoder
• Family of AE:
• Stack AutoEncoder (SAE) :: Use data itself as label / z = f(x), x=g(z) → x = g(f(x))
• Denoising autoencoder (DAE) :: Add random noise to input data
• Variational autoencoder (VAE) :: Generative Model + Stacked Autoencoder
• …
Sample Code:
https://github.com/buriburisuri/sugartensor/blob/master/sugart
ensor/example/mnist_dae.py
35. Mohammad Khalooei | khalooei@aut.ac.ir 35
AutoEncoder
• Family of AE:
• Stack AutoEncoder (SAE) :: Use data itself as label / z = f(x), x=g(z) → x = g(f(x))
• Denoising autoencoder (DAE) :: Add random noise to input data
• Variational autoencoder (VAE) :: Generative Model + Stacked Autoencoder
• … • Based on Variational approximation
• Kingma et al, “Auto-Encoding Variational Bayes”, 2013
Generative Adversarial Network
Train
ing
phases
36. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 36
AutoEncoder
• Family of AE:
• Stack AutoEncoder (SAE) :: Use data itself as label / z = f(x), x=g(z) → x = g(f(x))
• Denoising autoencoder (DAE) :: Add random noise to input data
• Variational autoencoder (VAE) :: Generative Model + Stacked Autoencoder
• … • Based on Variational approximation
• Kingma et al, “Auto-Encoding Variational Bayes”, 2013
Generating phases
37. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 37
AutoEncoder
• Family of AE:
• Stack AutoEncoder (SAE) :: Use data itself as label / z = f(x), x=g(z) → x = g(f(x))
• Denoising autoencoder (DAE) :: Add random noise to input data
• Variational autoencoder (VAE) :: Generative Model + Stacked Autoencoder
• … • Based on Variational approximation
• Kingma et al, “Auto-Encoding Variational Bayes”, 2013
• Reparameterization trick
• Enable back propagation
• Reduce variances of gradients
38. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 38
AutoEncoder
• Family of AE:
• Stack AutoEncoder (SAE) :: Use data itself as label / z = f(x), x=g(z) → x = g(f(x))
• Denoising autoencoder (DAE) :: Add random noise to input data
• Variational autoencoder (VAE) :: Generative Model + Stacked Autoencoder
• … • Based on Variational approximation
• Kingma et al, “Auto-Encoding Variational Bayes”, 2013
• Reparameterization trick
• Enable back propagation
• Reduce variances of gradients
39. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 39
AutoEncoder
• Family of AE:
• Stack AutoEncoder (SAE) :: Use data itself as label / z = f(x), x=g(z) → x = g(f(x))
• Denoising autoencoder (DAE) :: Add random noise to input data
• Variational autoencoder (VAE) :: Generative Model + Stacked Autoencoder
• … • Based on Variational approximation
• Kingma et al, “Auto-Encoding Variational Bayes”, 2013
40. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 40
AutoEncoder
• Family of AE:
• Stack AutoEncoder (SAE) :: Use data itself as label / z = f(x), x=g(z) → x = g(f(x))
• Denoising autoencoder (DAE) :: Add random noise to input data
• Variational autoencoder (VAE) :: Generative Model + Stacked Autoencoder
• …
(Namjukim – 2017) (Namjukim – 2017)
42. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 42
Review:: Generative Model
https://www.slideshare.net/BrianKim244/dcgan-77452250
43. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 43
Review:: Generative Model
https://www.slideshare.net/BrianKim244/dcgan-77452250
Distribution of the actual images
44. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 44
Review:: Generative Model
https://www.slideshare.net/BrianKim244/dcgan-77452250
45. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 45
Review:: Generative Model
https://www.slideshare.net/BrianKim244/dcgan-77452250
46. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 46
Review:: Generative Model
https://www.slideshare.net/BrianKim244/dcgan-77452250
47. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 47
Review:: Generative Model
https://www.slideshare.net/BrianKim244/dcgan-77452250
Distribution of the actual images
48. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 48
Contents
o Machine learning
o Supervised learning
o Unsupervised learning
o Generative vs. Discriminative models
o Generative Adversarial Network
o Introduction
o Definition
o Challenges
o Applications
o Tricks for training
49. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 49
Adversarial Nets :: Introduction
Ian Goodfellow et al, “Generative
Adversarial Networks”, 2014
87. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 87
GAN ideas (review intuitive papers)
https://carpedm20.github.io/faces/ https://github.com/carpedm20/DCGAN-tensorflow
DCGAN Deep convolutional generative adversarial network (DCGAN)
88. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 88
GAN ideas (review intuitive papers)
Vector space arithmetic
89. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 89
GAN ideas (review intuitive papers)
Vector space arithmetic
90. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 90
GAN ideas (review intuitive papers)
Super-Resolution
91. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 91
GAN ideas (review intuitive papers)
Super-Resolution
https://www.youtube.com/watch?v=9c4z6YsBGQ0
92. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 92
GAN ideas (review intuitive papers)
Conditional Generative Adversarial Network
93. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 93
GAN ideas (review intuitive papers)
Invertible Conditional GANs for image editing
94. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 94
GAN ideas (review intuitive papers)
Image to Image translation with conditional generative networks
https://phillipi.github.io/pix2pix/
95. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 95
GAN ideas (review intuitive papers)
Image to Image translation with conditional generative networks
https://phillipi.github.io/pix2pix/
96. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 96
GAN ideas (review intuitive papers)
Image to Image translation with conditional generative networks
https://phillipi.github.io/pix2pix/
97. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 97
GAN ideas (review intuitive papers)
Image to Image translation with conditional generative networks
https://phillipi.github.io/pix2pix/
98. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 98
GAN ideas (review intuitive papers)
Cycle GAN
F(G(X)) ≈ X
G: X → Y
F: Y → X
99. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 99
GAN ideas (review intuitive papers)
Cycle GAN
F(G(X)) ≈ X
G: X → Y
F: Y → X
100. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 100
GAN ideas (review intuitive papers)
Cycle GAN
F(G(X)) ≈ X
G: X → Y
F: Y → X
101. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 101
GAN ideas (review intuitive papers)
Cycle GAN
F(G(X)) ≈ X
G: X → Y
F: Y → X
102. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 102
GAN ideas (review intuitive papers)
Cycle GAN
F(G(X)) ≈ X
G: X → Y
F: Y → X
103. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 103
GAN ideas (review intuitive papers)
Cycle GAN
F(G(X)) ≈ X
G: X → Y
F: Y → X
104. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 104
GAN ideas (review intuitive papers)
Cycle GAN
F(G(X)) ≈ X
G: X → Y
F: Y → X
105. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 105
GAN ideas (review intuitive papers)
Unsupervised cross-domain image generation
106. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 106
GAN ideas (review intuitive papers)
Denoising GAN
107. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 107
GAN ideas (review intuitive papers)
Review:: Super resolution (SRGAN)
https://github.com/zsdonghao/SRGAN
108. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 108
GAN ideas (review intuitive papers)
Text to Image
109. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 109
GAN ideas (review intuitive papers)
Text to Image
110. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 110
GAN ideas (review intuitive papers)
MoCoGAN: Decomposing Motion and Content for Video Generation
https://github.com/sergeytulyakov/mocogan
111. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 111
GAN ideas (review intuitive papers)
MoCoGAN: Decomposing Motion and Content for Video Generation
https://github.com/sergeytulyakov/mocogan
112. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 112
GAN ideas (review intuitive papers)
ALOCC :: Adversarially Learned One-Class Classifier for Novelty Detection
https://github.com/khalooei/ALOCC-CVPR2018
113. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 113
GAN ideas (review intuitive papers)
ALOCC :: Adversarially Learned One-Class Classifier for Novelty Detection
https://github.com/khalooei/ALOCC-CVPR2018
114. • Converging
• Mode collapse
• Counting
…
Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 114
GAN challenges
116. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 116
GAN’s Applications
3.5 Years of Progress on Faces
(Brundage et al, 2018) (Goodfellow 2018)
117. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 117
GAN’s Applications
(Brundage et al, 2018) (Goodfellow 2018)
< 2 Years of Progress on Faces
118. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 118
GAN’s Applications
(Zhang et al., 2018) (Goodfellow 2018)
Self-Attention GAN
119. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 119
GAN’s Applications
(Goodfellow 2018)
Some intuitive:
Depth and Convolution
Class-conditional generation
Spectral Normalization
Hinge loss
Two-timescale update rule
Self-attention
120. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 120
GAN’s Applications
(Goodfellow 2018)
Some intuitive:
Depth and Convolution
Class-conditional generation
Hinge loss
Two-timescale update rule
Self-attention
No Convolution Needed to Solve Simple Tasks
Original GAN, 2014
121. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 121
GAN’s Applications
(Goodfellow 2018)
Some intuitive:
Depth and Convolution
Class-conditional generation
Hinge loss
Two-timescale update rule
Self-attention
Class-Conditional GANs
(Mirza and Osindero, 2014)
122. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 122
GAN’s Applications
(Goodfellow 2018)
Some intuitive:
Depth and Convolution
Class-conditional generation
Hinge loss
Two-timescale update rule
Self-attention
Class-Conditional GANs
(Odena et al, 2016)
AC-GAN: Specialist Generators
123. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 123
GAN’s Applications
(Goodfellow 2018)
Some intuitive:
Depth and Convolution
Class-conditional generation
Hinge loss
Two-timescale update rule
Self-attention
(Miyato et al, 2017)
Class-Conditional GANs
SN-GAN: Shared Generator
124. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 124
GAN’s Applications
(Goodfellow 2018)
Some intuitive:
Depth and Convolution
Class-conditional generation
Hinge loss
Two-timescale update rule
Self-attention
(Miyato et al 2017, Lim and Ye 2017, Tran et al 2017)
Hinge Loss
125. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 125
GAN’s Applications
(Goodfellow 2018)
Some intuitive:
Depth and Convolution
Class-conditional generation
Hinge loss
Two-timescale update rule
Self-attention
126. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 126
Thank you!
Mohammad Khalooei
Mkhalooei [at] gmail.com
Khalooei [at] aut.ac.ir
https://ceit.aut.ac.ir/~khalooei