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Generative Adversarial Networks Projects
GenerativeAdversarialNetworks(GANs)havethepotentialtobuildnext-generationmodels,astheycanmimicanydistributionofdata.Majorresearchanddevelopmentworkisbeingundertakeninthisfieldsinceitisoneoftherapidlygrowingareasofmachinelearning.Thisbookwilltestunsupervisedtechniquesfortrainingneuralnetworksasyoubuildsevenend-to-endprojectsintheGANdomain.GenerativeAdversarialNetworkProjectsbeginsbycoveringtheconcepts,tools,andlibrariesthatyouwillusetobuildefficientprojects.Youwillalsouseavarietyofdatasetsforthedifferentprojectscoveredinthebook.Thelevelofcomplexityoftheoperationsrequiredincreaseswitheverychapter,helpingyougettogripswithusingGANs.Youwillcoverpopularapproachessuchas3D-GAN,DCGAN,StackGAN,andCycleGAN,andyou’llgainanunderstandingofthearchitectureandfunctioningofgenerativemodelsthroughtheirpracticalimplementation.Bytheendofthisbook,youwillbereadytobuild,train,andoptimizeyourownend-to-endGANmodelsatworkorinyourownprojects.
目錄(280章)
倒序
- coverpage
- Title Page
- Copyright and Credits
- Generative Adversarial Networks Projects
- About Packt
- Why subscribe?
- Packt.com
- Contributors
- About the author
- About the reviewer
- Packt is searching for authors like you
- 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
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時(shí)間：2021-07-02 13:39:35
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