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Generative Adversarial Network

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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.

Publicado en: Software
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Generative Adversarial Network

  1. 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. 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. 3. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 3 GAN Zoo! https://github.com/hindupuravinash/the-gan-zoo
  4. 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. 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. 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. 7. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 7 GAN ! https://goo.gl/oCdBRj https://goo.gl/ibYzBr
  8. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
  19. 19. Supervised learning Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 19 Introduction f : y = f(x) Good Bad
  20. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 30. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 30 Generative Modeling Sample GeneratorTraining Data Training Data Density function Sample Generation Density Estimation
  31. 31. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 31 AutoEncoder http://curiousily.com/data-science/2017/02/02/what-to-do-when-data-is-missing-part-2.html
  32. 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. 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. 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. 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. 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. 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. 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. 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. 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)
  41. 41. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 41 Review …
  42. 42. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 42 Review:: Generative Model https://www.slideshare.net/BrianKim244/dcgan-77452250
  43. 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. 44. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 44 Review:: Generative Model https://www.slideshare.net/BrianKim244/dcgan-77452250
  45. 45. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 45 Review:: Generative Model https://www.slideshare.net/BrianKim244/dcgan-77452250
  46. 46. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 46 Review:: Generative Model https://www.slideshare.net/BrianKim244/dcgan-77452250
  47. 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. 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. 49. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 49 Adversarial Nets :: Introduction Ian Goodfellow et al, “Generative Adversarial Networks”, 2014
  50. 50. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 50 Adversarial Nets :: Definition https://techcrunch.com/2017/06/20/gangogh
  51. 51. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 51 Adversarial Nets :: Definition
  52. 52. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 52 Adversarial Nets :: Definition Tries to generates more real- likefake bills Tries tocatchfake bills Penalty if failure
  53. 53. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 53 Adversarial Nets :: Definition Tries to generates more real- likefake bills Tries tocatchfake bills Penalty if failure
  54. 54. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 54 Adversarial Nets :: Definition Tries to generates more real- likefake bills Tries tocatchfake bills Penalty if failure
  55. 55. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 55 Adversarial Nets :: Definition Tries to generates more real- likefake bills Tries tocatchfake bills Penalty if failure
  56. 56. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 56 Adversarial Nets :: Definition Tries to generates more real- likefake bills Tries tocatchfake bills Penalty if failure
  57. 57. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 57 Adversarial Nets :: Definition Tries to generates more real- likefake bills Tries tocatchfake bills Penalty if failure
  58. 58. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 58 Adversarial Nets :: Definition Tries to generates more real- likefake bills Tries tocatchfake bills Penalty if failure
  59. 59. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 59 Adversarial Nets :: Definition Tries to generates more real- likefake bills Tries tocatchfake bills Penalty if failure
  60. 60. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 60 Adversarial Nets :: Definition Tries to generates more real- likefake bills Tries tocatchfake bills Penalty if failure
  61. 61. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 61 Adversarial Nets :: Definition Tries to generates more real- likefake bills Tries tocatchfake bills Penalty if failure
  62. 62. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 62 Adversarial Nets :: Definition Tries to generates more real- likefake bills Tries tocatchfake bills Penalty if failure
  63. 63. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 63 Adversarial Nets :: Definition Tries to generates more real- likefake bills Tries tocatchfake bills Penalty if failure
  64. 64. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 64 Adversarial Nets :: Definition Tries to generates more real- likefake bills
  65. 65. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 65 Adversarial Nets :: Definition Tries tocatchfake bills Penalty if failure
  66. 66. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 66 Adversarial Nets :: Definition Tries tocatchfake bills Penalty if failure
  67. 67. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 67 Adversarial Nets :: Definition https://github.com/dmonn/GAN-face-generator
  68. 68. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 68 Adversarial Nets :: Definition
  69. 69. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 69 Adversarial Nets :: Framework (Goodfellow et al., 2014)
  70. 70. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 70
  71. 71. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 71
  72. 72. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 72
  73. 73. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 73
  74. 74. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 74
  75. 75. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 75
  76. 76. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 76
  77. 77. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 77
  78. 78. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 78 AutoEncoder :
  79. 79. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 79
  80. 80. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 80
  81. 81. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 81
  82. 82. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 82
  83. 83. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 83 • Different distance metrics : KL-divergence JS-divergence Earth-mover distance (Wasserstein distance) Total variation distance Hellinger distance Mahalanobis distance Bhattacharyya distance Energy distance …
  84. 84. • Pure GAN’s measure for difference :: Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 84
  85. 85. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 85 Namjukim - 2017
  86. 86. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 86 Namjukim - 2017
  87. 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. 88. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 88 GAN ideas (review intuitive papers) Vector space arithmetic
  89. 89. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 89 GAN ideas (review intuitive papers) Vector space arithmetic
  90. 90. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 90 GAN ideas (review intuitive papers) Super-Resolution
  91. 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. 92. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 92 GAN ideas (review intuitive papers) Conditional Generative Adversarial Network
  93. 93. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 93 GAN ideas (review intuitive papers) Invertible Conditional GANs for image editing
  94. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 105. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 105 GAN ideas (review intuitive papers) Unsupervised cross-domain image generation
  106. 106. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 106 GAN ideas (review intuitive papers) Denoising GAN
  107. 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. 108. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 108 GAN ideas (review intuitive papers) Text to Image
  109. 109. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 109 GAN ideas (review intuitive papers) Text to Image
  110. 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. 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. 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. 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. 114. • Converging • Mode collapse • Counting … Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 114 GAN challenges
  115. 115. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 115 GAN’s Applications
  116. 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. 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. 118. Generative Adversarial Network Mohammad Khalooei | khalooei@aut.ac.ir 118 GAN’s Applications (Zhang et al., 2018) (Goodfellow 2018) Self-Attention GAN
  119. 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. 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. 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. 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. 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. 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. 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. 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

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