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Brian Kim

Overview of Deep Convolutional Adversarial Networks

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Korea University, Department of Computer Science & Radio Communication Engineering 2016010646 Bumsoo Kim DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS Unsupervised Representation Learning with DCGAN Bumsoo Kim Presentation 2017 1
CONTENTS Bumsoo Kim Presentation 2017 2 1 2 3 4 GenerativeModels Modelarchitecture Evaluation Vectorarithmetic
CONTENTS Bumsoo Kim Presentation 2017 3 1 GenerativeModels 2 3 4 Modelarchitecture Evaluation Vectorarithmetic
Generative Models Bumsoo Kim Presentation 2017 4 Generative Models Training Data
Generative Models Bumsoo Kim Presentation 2017 5 Generative Models Generative Models Training Data
Generative Models Bumsoo Kim Presentation 2017 6 Generative Models Training Data Generated Data Unseen new data Generative Models
Generative Models Bumsoo Kim Presentation 2017 7 How does it work? Distribution of actual images
Generative Models Bumsoo Kim Presentation 2017 8 How does it work? Red apples
Generative Models Bumsoo Kim Presentation 2017 9 How does it work? Green apples
Generative Models Bumsoo Kim Presentation 2017 10 How does it work? Weird apples
Generative Models Bumsoo Kim Presentation 2017 11 How does it work? Distribution of actual images 𝒑𝒑 𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎(𝒙𝒙) Find a 𝒑𝒑 𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎 𝒙𝒙 that approximates the data distribution of the original images Goal
CONTENTS Bumsoo Kim Presentation 2017 12 1 2 3 4 GenerativeModels Modelarchitecture Evaluation Vectorarithmetic
Model architecture Bumsoo Kim Presentation 2017 13 Generative Adversarial Network Counterfeiter Police
Model architecture Bumsoo Kim Presentation 2017 14 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure
Model architecture Bumsoo Kim Presentation 2017 15 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure 100 100 100 100
Model architecture Bumsoo Kim Presentation 2017 16 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure 100 100 100 100
Model architecture Bumsoo Kim Presentation 2017 17 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure 100 100 100 100 100
Model architecture Bumsoo Kim Presentation 2017 18 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure 100 100 100 100 100
Model architecture Bumsoo Kim Presentation 2017 19 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure 100 100 100 100 100
Model architecture Bumsoo Kim Presentation 2017 20 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure 100 100 100 100 100
Model architecture Bumsoo Kim Presentation 2017 21 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure 100 100 100 100 100
Model architecture Bumsoo Kim Presentation 2017 22 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure
Model architecture Bumsoo Kim Presentation 2017 23 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure fake real
Model architecture Bumsoo Kim Presentation 2017 24 Generative Adversarial Network Counterfeiter Generator Tries to generates more real-like fake bills
Model architecture Bumsoo Kim Presentation 2017 25 Generative Adversarial Network Police Discriminator Discriminative Model Discriminative Model Tries to catch fake bills Penalty if failure conv1 conv2 conv3 conv4 true/false
CONTENTS Bumsoo Kim Presentation 2017 26 1 3 4 GenerativeModels Evaluation Vectorarithmetic 2 Modelarchitecture
Evaluation Bumsoo Kim Presentation 2017 27 Empirical validation “What I cannot create, I do not understand.” -Richard Feynman (4x4x1792) (1x28672)
Evaluation Bumsoo Kim Presentation 2017 28 Check out yourself!
CONTENTS Bumsoo Kim Presentation 2017 29 1 2 3 4 GenerativeModels Modelarchitecture Evaluation Vectorarithmetic
Vector arithmetic Bumsoo Kim Presentation 2017 30 Interpolation between series 1.00 0.72 3.35 … 2.21 0.17 2.00 0.72 3.35 … 2.21 0.17 front view side view interpolation
Vector arithmetic Bumsoo Kim Presentation 2017 31 Interpolation between series 3.13 0.72 3.35 … 2.21 0.17 3.00 0.72 3.35 … 2.21 0.17 7.00 0.72 3.35 … 2.21 0.17 7.13 0.72 3.35 … 2.21 0.17
THANK YOU FOR YOUR ATTENTION !! Bumsoo Kim Presentation 2017 32

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Dcgan

  • 1. Korea University, Department of Computer Science & Radio Communication Engineering 2016010646 Bumsoo Kim DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS Unsupervised Representation Learning with DCGAN Bumsoo Kim Presentation 2017 1
  • 2. CONTENTS Bumsoo Kim Presentation 2017 2 1 2 3 4 GenerativeModels Modelarchitecture Evaluation Vectorarithmetic
  • 3. CONTENTS Bumsoo Kim Presentation 2017 3 1 GenerativeModels 2 3 4 Modelarchitecture Evaluation Vectorarithmetic
  • 4. Generative Models Bumsoo Kim Presentation 2017 4 Generative Models Training Data
  • 5. Generative Models Bumsoo Kim Presentation 2017 5 Generative Models Generative Models Training Data
  • 6. Generative Models Bumsoo Kim Presentation 2017 6 Generative Models Training Data Generated Data Unseen new data Generative Models
  • 7. Generative Models Bumsoo Kim Presentation 2017 7 How does it work? Distribution of actual images
  • 8. Generative Models Bumsoo Kim Presentation 2017 8 How does it work? Red apples
  • 9. Generative Models Bumsoo Kim Presentation 2017 9 How does it work? Green apples
  • 10. Generative Models Bumsoo Kim Presentation 2017 10 How does it work? Weird apples
  • 11. Generative Models Bumsoo Kim Presentation 2017 11 How does it work? Distribution of actual images 𝒑𝒑 𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎(𝒙𝒙) Find a 𝒑𝒑 𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎𝒎 𝒙𝒙 that approximates the data distribution of the original images Goal
  • 12. CONTENTS Bumsoo Kim Presentation 2017 12 1 2 3 4 GenerativeModels Modelarchitecture Evaluation Vectorarithmetic
  • 13. Model architecture Bumsoo Kim Presentation 2017 13 Generative Adversarial Network Counterfeiter Police
  • 14. Model architecture Bumsoo Kim Presentation 2017 14 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure
  • 15. Model architecture Bumsoo Kim Presentation 2017 15 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure 100 100 100 100
  • 16. Model architecture Bumsoo Kim Presentation 2017 16 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure 100 100 100 100
  • 17. Model architecture Bumsoo Kim Presentation 2017 17 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure 100 100 100 100 100
  • 18. Model architecture Bumsoo Kim Presentation 2017 18 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure 100 100 100 100 100
  • 19. Model architecture Bumsoo Kim Presentation 2017 19 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure 100 100 100 100 100
  • 20. Model architecture Bumsoo Kim Presentation 2017 20 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure 100 100 100 100 100
  • 21. Model architecture Bumsoo Kim Presentation 2017 21 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure 100 100 100 100 100
  • 22. Model architecture Bumsoo Kim Presentation 2017 22 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure
  • 23. Model architecture Bumsoo Kim Presentation 2017 23 Generative Adversarial Network Counterfeiter Police Generator Discriminator Tries to generates more real-like fake bills Tries to catch fake bills Penalty if failure fake real
  • 24. Model architecture Bumsoo Kim Presentation 2017 24 Generative Adversarial Network Counterfeiter Generator Tries to generates more real-like fake bills
  • 25. Model architecture Bumsoo Kim Presentation 2017 25 Generative Adversarial Network Police Discriminator Discriminative Model Discriminative Model Tries to catch fake bills Penalty if failure conv1 conv2 conv3 conv4 true/false
  • 26. CONTENTS Bumsoo Kim Presentation 2017 26 1 3 4 GenerativeModels Evaluation Vectorarithmetic 2 Modelarchitecture
  • 27. Evaluation Bumsoo Kim Presentation 2017 27 Empirical validation “What I cannot create, I do not understand.” -Richard Feynman (4x4x1792) (1x28672)
  • 28. Evaluation Bumsoo Kim Presentation 2017 28 Check out yourself!
  • 29. CONTENTS Bumsoo Kim Presentation 2017 29 1 2 3 4 GenerativeModels Modelarchitecture Evaluation Vectorarithmetic
  • 30. Vector arithmetic Bumsoo Kim Presentation 2017 30 Interpolation between series 1.00 0.72 3.35 … 2.21 0.17 2.00 0.72 3.35 … 2.21 0.17 front view side view interpolation
  • 31. Vector arithmetic Bumsoo Kim Presentation 2017 31 Interpolation between series 3.13 0.72 3.35 … 2.21 0.17 3.00 0.72 3.35 … 2.21 0.17 7.00 0.72 3.35 … 2.21 0.17 7.13 0.72 3.35 … 2.21 0.17
  • 32. THANK YOU FOR YOUR ATTENTION !! Bumsoo Kim Presentation 2017 32