| Description |
1 online resource (310 pages) |
| Physical Medium |
polychrome |
| Description |
text file |
| Contents |
Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: 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 |
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The Fréchet inception distanceVariants 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; Chapter 2: 3D-GAN -- Generating Shapes Using GANs; Introduction to 3D-GANs; 3D convolutions; The architecture of a 3D-GAN |
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The architecture of the generator networkThe 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 |
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Chapter 3: Face Aging Using Conditional GANIntroducing 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 |
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Training the cGANInitial latent vector approximation; Latent vector optimization; Visualizing the losses; Visualizing the graphs; Practical applications of Age-cGAN; Summary; Chapter 4: 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 |
| Note |
Training the DCGAN |
| Summary |
In this book, we will use different complexities of datasets in order to build end-to-end projects. With every chapter, the level of complexity and operations will become advanced. It consists of 8 full-fledged projects covering approaches such as 3D-GAN, Age-cGAN, DCGAN, SRGAN, StackGAN, and CycleGAN with real-world use cases. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Machine learning.
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Machine learning. |
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Neural networks (Computer science)
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Neural networks (Computer science) |
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Artificial intelligence.
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Artificial intelligence. |
| Genre/Form |
Electronic books.
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| Other Form: |
Print version: Ahirwar, Kailash. Generative Adversarial Networks Projects : Build Next-Generation Generative Models Using TensorFlow and Keras. Birmingham : Packt Publishing Ltd, ©2019 9781789136678 |
| ISBN |
9781789134193 (electronic book) |
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1789134196 (electronic book) |
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