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Title Page
Copyright and Credits
Generative Adversarial Networks Projects
About Packt
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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
Conventions used
Get in touch
Reviews
Introduction to Generative Adversarial Networks
What is a GAN?
What is a generator network?
What is a discriminator network?
Training through adversarial play in GANs
Practical applications of GANs
The detailed architecture of a GAN
The architecture of the generator
The architecture of the discriminator
Important concepts related to GANs
Kullback-Leibler divergence
Jensen-Shannon divergence
Nash equilibrium
Objective functions
Scoring algorithms
The inception score
The Fréchet inception distance
Variants of GANs
Deep convolutional generative adversarial networks
StackGANs
CycleGANs
3D-GANs
Age-cGANs
pix2pix
Advantages of GANs
Problems with training GANs
Mode collapse
Vanishing gradients
Internal covariate shift
Solving stability problems when training GANs
Feature matching
Mini-batch discrimination
Historical averaging
One-sided label smoothing
Batch normalization
Instance normalization
Summary
3D-GAN - Generating Shapes Using GANs
Introduction to 3D-GANs
3D convolutions
The architecture of a 3D-GAN
The architecture of the generator network
The architecture of the discriminator network
Objective function
Training 3D-GANs
Setting up a project
Preparing the data
Download and extract the dataset
Exploring the dataset
What is a voxel?
Loading and visualizing a 3D image
Visualizing a 3D image
A Keras implementation of a 3D-GAN
The generator network
The discriminator network
Training a 3D-GAN
Training the networks
Saving the models
Testing the models
Visualizing losses
Visualizing graphs
Hyperparameter optimization
Practical applications of 3D-GANs
Summary
Face Aging Using Conditional GAN
Introducing cGANs for face aging
Understanding cGANs
The architecture of the Age-cGAN
The encoder network
The generator network
The discriminator network
Face recognition network
Stages of the Age-cGAN
Conditional GAN training
The training objective function
Initial latent vector approximation
Latent vector optimization
Setting up the project
Preparing the data
Downloading the dataset
Extracting the dataset
A Keras implementation of an Age-cGAN
The encoder network
The generator network
The discriminator network
Training the cGAN
Training the cGAN
Initial latent vector approximation
Latent vector optimization
Visualizing the losses
Visualizing the graphs
Practical applications of Age-cGAN
Summary
Generating Anime Characters Using DCGANs
Introducing to DCGANs
Architectural details of a DCGAN
Configuring the generator network
Configuring the discriminator network
Setting up the project
Downloading and preparing the anime characters dataset
Downloading the dataset
Exploring the dataset
Cropping and resizing images in the dataset
Implementing a DCGAN using Keras
Generator
Discriminator
Training the DCGAN
Loading the samples
Building and compiling the networks
Training the discriminator network
Training the generator network
Generating images
Saving the model
Visualizing generated images
Visualizing losses
Visualizing graphs
Tuning the hyperparameters
Practical applications of DCGAN
Summary
Using SRGANs to Generate Photo-Realistic Images
Introducing SRGANs
The architecture of SRGANs
The architecture of the generator network
The architecture of the discriminator network
The training objective function
Content loss
Pixel-wise MSE loss
VGG loss
Adversarial loss
Setting up the project
Downloading the CelebA dataset
The Keras implementation of SRGAN
The generator network
The discriminator network
VGG19 network
The adversarial network
Training the SRGAN
Building and compiling the networks
Training the discriminator network
Training the generator network
Saving the models
Visualizing generated images
Visualizing losses
Visualizing graphs
Practical applications of SRGANs
Summary
StackGAN - Text to Photo-Realistic Image Synthesis
Introduction to StackGAN
Architecture of StackGAN
The text encoder network
The conditioning augmentation block
Getting the conditioning augmentation variable
Stage-I
The generator network
The discriminator network
Losses for Stage-I of StackGAN
Stack-II
The generator network
The discriminator network
Losses for Stage-II of StackGAN
Setting up the project
Data preparation
Downloading the dataset
Extracting the dataset
Exploring the dataset
A Keras implementation of StackGAN
Stage-I
Text encoder network
Conditional augmentation network
The generator network
The discriminator network
The adversarial model
Stage-II
Generator network
Downsampling blocks
The residual blocks
Upsampling Blocks
The discriminator network
Downsampling blocks
The concatenation block
The fully connected classifier
Training a StackGAN
Training the Stage-I StackGAN
Loading the dataset
Creating models
Training the model
Training the Stage-II StackGAN
Loading the dataset
Creating models
Training the model
Visualizing the generated images
Visualizing losses
Visualizing the graphs
Practical applications of StackGAN
Summary
CycleGAN - Turn Paintings into Photos
An introduction to CycleGANs
The architecture of a CycleGAN
The architecture of the generator
The architecture of the discriminator
The training objective function
Adversarial loss
Cycle consistency loss
Full objective function
Setting up the project
Downloading the dataset
Keras implementation of CycleGAN
The generator network
The discriminator network
Training the CycleGAN
Loading the dataset
Building and compiling the networks
Creating and compiling an adversarial network
Starting the training
Training the discriminator networks
Training the adversarial network
Saving the model
Visualizing the images generated
Visualizing losses
Visualizing the graphs
Practical applications of CycleGANs
Summary
Further reading
Conditional GAN - Image-to-Image Translation Using Conditional Adversarial Networks
Introducing Pix2pix
The architecture of pix2pix
The generator network
The encoder network
The decoder network
The discriminator network
The training objective function
Setting up the project
Preparing the data
Visualizing images
A Keras implementation of pix2pix
The generator network
The discriminator network
The adversarial network
Training the pix2pix network
Saving the models
Visualizing the generated images
Visualizing the losses
Visualizing the graphs
Practical applications of a pix2pix network
Summary
Predicting the Future of GANs
Our predictions about the future of GANs
Improving existing deep learning methods
The evolution of the commercial applications of GANs
Maturation of the GAN training process
Potential future applications of GANs
Creating infographics from text
Generating website designs
Compressing data
Drug discovery and development
GANs for generating text
GANs for generating music
Exploring GANs
Summary
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