Generative Adversarial Networks Projects

Title Page
Copyright and Credits
	Generative Adversarial Networks Projects
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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
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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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