Hands-On Generative Adversarial Networks with Keras

Title Page
Copyright and Credits
   Hands-On Generative Adversarial Networks with Keras
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Foreword
Contributors
   About the author
   About the reviewer
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Preface
   Who this book is for
   What this book covers
   To get the most out of this book
      Download the example code files
      Download the color images
      Conventions used
   Get in touch
      Reviews
Section 1: Introduction and Environment Setup
Deep Learning Basics and Environment Setup
   Deep learning basics
      Artificial Neural Networks (ANNs)
      The parameter estimation
      Backpropagation
      Loss functions
         L1 loss
         L2 loss
         Categorical crossentropy loss
      Non-linearities
         Sigmoid
         Tanh
         ReLU
      A fully connected layer
      The convolution layer
      The max pooling layer
   Deep learning environment setup
      Installing Anaconda and Python
      Setting up a virtual environment in Anaconda
      Installing TensorFlow
      Installing Keras
      Installing data visualization and machine learning libraries
         The matplotlib library
         The Jupyter library
         The scikit-learn library
      NVIDIA\'s CUDA Toolkit and cuDNN
   The deep learning environment test
   Summary
Introduction to Generative Models
   Discriminative and generative models compared
      Comparing discriminative and generative models
   Generative models
      Autoregressive models
      Variational autoencoders
      Reversible flows
      Generative adversarial networks
   GANs – building blocks
      The discriminator
      The generator
      Real and fake data
      Random noise
      Discriminator and generator loss
   GANs – strengths and weaknesses
   Summary
Section 2: Training GANs
Implementing Your First GAN
   Technical requirements
   Imports
   Implementing a Generator and Discriminator
      Generator
      Discriminator
   Auxiliary functions
   Training your GAN
   Summary
   Further reading
Evaluating Your First GAN
   The evaluation of GANs
      Image quality
      Image variety
      Domain specifications
   Qualitative methods
      k-nearest neighbors
      Mode analysis
      Other methods
   Quantitative methods
      The Inception score
      The Frechét Inception Distance
      Precision, Recall, and the F1 Score
   GANs and the birthday paradox
   Summary
Improving Your First GAN
   Technical requirements
   Challenges in training GANs
      Mode collapse and mode drop
      Training instability
      Sensitivity to hyperparameter initialization
      Vanishing gradients
   Tricks of the trade
      Tracking failure
      Working with labels
      Working with discrete inputs
      Adding noise
      Input normalization
      Modified objective function
      Distribute latent vector
      Weight normalization
      Avoid sparse gradients
      Use a different optimizer
      Learning rate schedule
   GAN model architectures
      ResNet GAN
   GAN algorithms and loss functions
      Least Squares GAN
      Wasserstein GAN
      Wasserstein GAN with gradient penalty
      Relativistic GAN
   Summary
Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio
Progressive Growing of GANs
   Technical requirements
   Progressive Growing of GANs
      Increasing variation using minibatch standard deviation
      Normalization in the generator and the discriminator
      Pixelwise feature vector normalization in the generator
   Experimental setup
      Training
         Helper functions
         Initializations
         Training loops
   Model implementation
      Custom layers
      The discriminator
      The generator
      GANs
   Summary
Generation of Discrete Sequences Using GANs
   Technical requirements
   Natural language generation with GANs
   Experimental setup
      Data
      Auxiliary training functions
      Training
         Imports and global variables
         Initializations
         Training loop
      Logging
   Model implementation
      Helper functions
      Discriminator
      Generator
   Inference
      Model trained on words
      Model trained on characters
   Summary
Text-to-Image Synthesis with GANs
   Technical Requirements
   Text-to-image synthesis
   Experimental setup
      Data utils
      Logging utils
      Training
         Initial setup
         The training loop
   Model implementation
      Wrapper
      Discriminator
      Generator
   Improving the baseline model
      Training
   Inference
      Sampling the generator
      Interpolation in the Latent Space
      Interpolation in the text-embedding space
      Inferencing with arithmetic in the text-embedding space
   Summary
TequilaGAN - Identifying GAN Samples
   Technical requirements
   Identifying GAN samples
      Related work
   Feature extraction
      Centroid
      Slope
   Metrics
      Jensen-Shannon divergence
      Kolgomorov-Smirnov Two-Sample test
   Experiments
      MNIST
   Summary
   References
Whats next in GANs
   What we\'ve GANed so far
      Generative models
      Architectures
      Loss functions
      Tricks of the trade
      Implementations
   Unanswered questions in GANs
      Are some losses better than others?
      Do GANs do distribution learning?
      All about that inductive bias
      How can you kill a GAN?
   Artistic GANs
      Visual arts
         GANGogh
         Image inpainting
         Vid2Vid
         GauGAN
      Sonic arts
         MuseGAN
         GANSynth
   Recent and yet-to-be-explored GAN topics
   Summary
   Closing remarks
   Further reading