更新时间：2021-07-02 13:39:35
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Generative Adversarial Networks Projects
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Preface
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What this book covers
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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
Face Aging Using Conditional GAN
Introducing cGANs for face aging
Understanding cGANs
The architecture of the Age-cGAN
The encoder 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
Downloading the dataset
Extracting the dataset
