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Generative Adversarial Networks (GAN) - Introduction and Experimentation

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Brief technical introduction to Generative Adversarial Networks, their background and advanced models, with graphical representations of models and experimentation on various datasets.

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Generative Adversarial Networks (GAN) - Introduction and Experimentation

  1. 1. Generative Adversarial Networks Introduction and Experimentation Alexander Lattas alexandros.lattas17@imperial.ac.uk Background Generative Adversarial Networks Models Experiments Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018
  2. 2. Background Neural Networks and Deep Learning Background Generative Adversarial Networks Models Experiments Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018
  3. 3. Background Generative Adversarial Networks Models Experiments Neural Networks Convolution Loss Functions Generative Models Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 Neural Network (NN) Deep Belief Network (DBN) Input Neurons: Output Neurons: Loss: Back-Propagation Gradients: Neural Network Maths Rumelhart et al, 1986 Hinton et al., 2006 Rumelhart et al, 1986
  4. 4. Background Generative Adversarial Networks Models Experiments Neural Networks Convolution Loss Functions Generative Models Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 A Convolutional Neural Network (CNN) CNN Loss Function Weight Sharing Convolution Subsampling Fully Connected LeCun et al., 1998 LeCun et al., 1998
  5. 5. For Markov Chain Approx. (RBM): Loss Function (Kullback-Leibler Div.): Only one iteration is enough. Find most probable model for the input data: Loss E: Energy: violation of data constraints Background Generative Adversarial Networks Models Experiments Neural Networks Convolution Loss Functions Generative Models Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 Maximum Likelihood Estimation Contrastive Divergence Hinton et al, 2006
  6. 6. Promising results in image generation Lower quality Blurry images Background Generative Adversarial Networks Models Experiments Neural Networks Convolution Loss Functions Generative Models Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 Variational Auto-encoder (VAE) Kingma et al., 2013 Fully Connected Belief Network Samples one at a time Non-Linear Indep. Component Analysis restricted modeling Tractable Explicit Density Oord et al., 2016Deco et al., 2016 Markov Chain Approximation Slow Cannot confirm convergence Approximated Explicit Density Hinton et al, 2006
  7. 7. Background Generative Adversarial Networks Models Experiments Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 Generative Adversarial Networks
  8. 8. Background Generative Adversarial Networks Models Experiments Model Training Loss Functions Game Theory Challenges Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 Generative Adversarial Network (GAN) Goodfellow et al, 2014 GAN Value Function Goodfellow et al, 2014
  9. 9. At optimality, the discriminator finds: The generator produces: So the discriminator cannot decide: Background Generative Adversarial Networks Models Experiments Model Training Loss Functions Game Theory Challenges Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 Stochastic Gradient Descent Training for GANs Goodfellow et al, 2014 GAN Equilibrium: Goodfellow et al, 2014
  10. 10. proven, interesting in theory not useful in Deep NN works with Deep NN Background Generative Adversarial Networks Models Experiments Model Training Loss Functions Game Theory Challenges Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 Discriminator Loss Minimax Generator Loss Heuristic Generator Loss Goodfellow et al, 2014 Goodfellow, 2016 Goodfellow, 2016
  11. 11. Discriminator Loss Generator Loss Learning in a zero-sum environment Background Generative Adversarial Networks Models Experiments Model Training Loss Functions Game Theory Challenges Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 G D
  12. 12. Background Generative Adversarial Networks Models Experiments Model Training Loss Functions Game Theory Challenges Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 Each agent undoes progress of the other. Not proven for Deep NN Convergence Goodfellow, 2016 Generator makes only few types of data. Minibatch Features Mode Collapse Salimans et al, 2016 No clear qualitative evaluation. Inception Score Model Evaluation Lucic et al., 2016
  13. 13. Yet another GAN model… Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 Background Generative Adversarial Networks Models Experiments
  14. 14. Deep Convolutional GAN (DCGAN) Background Generative Adversarial Networks Models Experiments DCGAN WGAN EBGAN BEGAN Model Evaluation Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 Fully convolutional layers No fully connected layers Batch Normalisation ReLU, LeakyReLU Radford et al., 2015
  15. 15. Continuous Loss Function with 𝑊 = [−0.01, 0.01] Strong gradients No Mode Collapse Background Generative Adversarial Networks Models Experiments DCGAN WGAN EBGAN BEGAN Model Evaluation Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 Wasserstein Distance Wasserstein GAN (WGAN) Loss Functions Arjovsky et al., 2017 Arjovsky et al., 2017
  16. 16. Continuity without W bias Background Generative Adversarial Networks Models Experiments DCGAN WGAN EBGAN BEGAN Model Evaluation Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 WGAN with Gradient Penalty (WGAN-GP) Gulrajani et al., 2017
  17. 17. Energy Based Loss Function Stable training Auto-encoder Discriminator High Resolution Output G: Low energy when 𝑝 𝑑𝑎𝑡𝑎 is close to 𝑝 𝑚𝑜𝑑𝑒𝑙 D: Low energy to 𝑝 𝑑𝑎𝑡𝑎 , high to 𝑝 𝑚𝑜𝑑𝑒𝑙 Hinge-type Discriminator Loss Heuristic Generator Loss Energy-Based GAN (EBGAN) Energy-Based Loss Function Background Generative Adversarial Networks Models Experiments DCGAN WGAN EBGAN BEGAN Model Evaluation Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 Zhao et al., 2016 Zhao et al., 2016
  18. 18. k acts as momentum on training λ is the learning rate for momentum γ is the desired equilibrium stable and quick training Enforces equilibrium Equilibrium Measure Boundary Equilibrium GAN (BEGAN) Convergence Measure Berthelot et al., 2017 Background Generative Adversarial Networks Models Experiments DCGAN WGAN EBGAN BEGAN Model Evaluation Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 Berthelot et al., 2017 Berthelot et al., 2017
  19. 19. Correlates with human judgement Can’t find intra-class mode drop Correlates with human judgement Finds intra-class mode drop FID finds no GAN model to outperform the other, given enough training and correct hyper-parameters Qualitative comparison of generated images Not robust Background Generative Adversarial Networks Models Experiments DCGAN WGAN EBGAN BEGAN Model Evaluation Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 Qualitative Evaluation Inception Score Gerhard et al., 2013 Salimans et al., 2016 Frechet Inception Distance Lucic et al., 2017
  20. 20. Experiments GAN, DCGAN, WGAN, EBGAN, BEGAN MNIST, CelebA, Cats, Cars Background Generative Adversarial Networks Models Experiments Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018
  21. 21. Dataset Background Generative Adversarial Networks Models Experiments Description GAN DCGAN WGAN WGAN-GP EBGAN BEGAN CelebA Cats Cars Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 Architecture Setup
  22. 22. Background Generative Adversarial Networks Models Experiments Description GAN DCGAN WGAN WGAN-GP EBGAN BEGAN CelebA Cats Cars Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018
  23. 23. Background Generative Adversarial Networks Models Experiments Description GAN DCGAN WGAN WGAN-GP EBGAN BEGAN CelebA Cats Cars Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 +75%
  24. 24. Background Generative Adversarial Networks Models Experiments Description GAN DCGAN WGAN WGAN-GP EBGAN BEGAN CelebA Cats Cars Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018
  25. 25. Background Generative Adversarial Networks Models Experiments Description GAN DCGAN WGAN WGAN-GP EBGAN BEGAN CelebA Cats Cars Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018
  26. 26. Background Generative Adversarial Networks Models Experiments Description GAN DCGAN WGAN WGAN-GP EBGAN BEGAN CelebA Cats Cars Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018
  27. 27. Background Generative Adversarial Networks Models Experiments Description GAN DCGAN WGAN WGAN-GP EBGAN BEGAN CelebA Cats Cars Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018 +47%
  28. 28. Background Generative Adversarial Networks Models Experiments Description GAN DCGAN WGAN WGAN-GP EBGAN BEGAN CelebA Cats Cars Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018
  29. 29. Background Generative Adversarial Networks Models Experiments Description GAN DCGAN WGAN WGAN-GP EBGAN BEGAN CelebA Cats Cars Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018
  30. 30. Background Generative Adversarial Networks Models Experiments Description GAN DCGAN WGAN WGAN-GP EBGAN BEGAN CelebA Cats Cars Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018
  31. 31. Background Generative Adversarial Networks Models Experiments Description GAN DCGAN WGAN WGAN-GP EBGAN BEGAN CelebA Cats Cars Alexander Lattas | MSc Advanced Computing @ Imperial College London11/05/2018

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