Hands-On Generative Adversarial Networks with PyTorch 1.x

Cover
Title Page
Copyright and Credits
About Packt
Contributors
Table of Contents
Preface
Section 1: Introduction to GANs and PyTorch
Chapter 1: Generative Adversarial Networks Fundamentals
	Fundamentals of machine learning
		Machine learning – classification and generation
		Introducing adversarial learning
	Generator and discriminator networks
		Mathematical background of GANs
		Using NumPy to train a sine signal generator
			Designing the network architectures
			Defining activation functions and the loss function
			Working on forward pass and backpropagation
			Training our GAN model
	What GAN we do?
		Image processing
			Image synthesis
			Image translation
			Video synthesis and translation
		NLP
		3D modeling
	Summary
	References and useful reading list
Chapter 2: Getting Started with PyTorch 1.3
	What's new in PyTorch 1.3?
		Easy switching from eager mode to graph mode
		The C++ frontend
		The redesigned distributed library
		Better research reproducibility
		Miscellaneous
			The PyTorch ecosystem
			Cloud support
			Migrating your previous code to 1.x
	CUDA – GPU acceleration for fast training and evaluation
		Installing NVIDIA driver
		Installing CUDA
		Installing cuDNN
		Evaluating your CUDA installation
	Installing PyTorch on Windows and Linux
		Setting up the Python environment
			Installing Python
			Installing Anaconda Python
			Prerequisites before we move on
		Installing PyTorch
			Installing official binaries
			Building Pytorch from source
		Evaluating your PyTorch installation
		Bonus: setting up VS Code for Python coding
			Configuring VS Code for Python development
			Recommended VS Code extensions
	References and useful reading list
	Summary
Chapter 3: Best Practices for Model Design and Training
	Model design cheat sheet
		Overall model architecture design
		Choosing a convolution operation method
		Choosing a downsampling operation method
		More on model design
	Model training cheat sheet
		Parameter initialization
		Adjusting the loss function
		Choosing an optimization method
		Adjusting the learning rate
		Gradient clipping, weight clipping, and more
	Efficient coding in Python
		Reinventing the wheel wisely
		Advice for beginners in deep learning
	Summary 
Section 2: Typical GAN Models for Image Synthesis
Chapter 4: Building Your First GAN with PyTorch
	Introduction to Deep Convolutional GANs
		The architecture of generator
		The architecture of a discriminator
	Creating a DCGAN with PyTorch
		Generator network
		Discriminator network
	Model training and evaluation
		Training iteration
		Visualizing generated samples
		Checking GPU usage information
	Moving to larger datasets
		Generating human faces from the CelebA dataset
		Generating bedroom photos from the LSUN dataset
	Having fun with the generator network
		Image interpolation
		Semantic vector arithmetic
	Summary
	References and useful reading list
Chapter 5: Generating Images Based on Label Information
	CGANs – how are labels used?
		Combining labels with the generator
		Integrating labels into the discriminator
	Generating images from labels with the CGAN
		One-stop model training API
		Argument parsing and model training
	Working with Fashion-MNIST
	InfoGAN – unsupervised attribute extraction
		Network definitions of InfoGAN
		Training and evaluation of InfoGAN
	References and useful reading list
	Summary
Chapter 6: Image-to-Image Translation and Its Applications
	Using pixel-wise labels to translate images with pix2pix
		Generator architecture
		Discriminator architecture
		Training and evaluation of pix2pix
	Pix2pixHD – high-resolution image translation
		Model architecture
		Model training
	CycleGAN – image-to-image translation from unpaired collections
		Cycle consistency-based model design
		Model training and evaluation
	Summary
	Furthering reading
Chapter 7: Image Restoration with GANs
	Image super-resolution with SRGAN
		Creating a generator
		Creating the discriminator
		Defining training loss
		Training SRGAN to generate high-resolution images
	Generative image inpainting
		Efficient convolution – from im2col to nn.Unfold
		WGAN – understanding the Wasserstein distance
			Analyzing the problems with vanilla GAN loss
			The advantages of Wasserstein distance
		Training GAN for image inpainting
			Model design for image inpainting
			Implementation of Wasserstein loss
	Summary
	Useful reading list and references
Chapter 8: Training Your GANs to Break Different Models
	Adversarial examples – attacking deep learning models
		What are adversarial examples and how are they created?
		Adversarial attacking with PyTorch
	Generative adversarial examples
		Preparing an ensemble classifier for Kaggle's Cats vs. Dogs
		Breaking the classifier with advGAN
	Summary
	References and further reading list
Chapter 9: Image Generation from Description Text
	Text-to-image synthesis with GANs
		Quick introduction to word embedding
		Translating text to image with zero-shot transfer learning
			Zero-shot learning
		GAN architecture and training
	Generating photo-realistic images with StackGAN++
		High-resolution text-to-image synthesis with StackGAN
		From StackGAN to StackGAN++
		Training StackGAN++ to generate images with better quality
	Summary 
	Further reading
Chapter 10: Sequence Synthesis with GANs
	Text generation via SeqGAN – teaching GANs how to tell jokes
		Design of SeqGAN – GAN, LSTM, and RL
			A quick introduction to RNN and LSTM
			Reinforcement learning versus supervised learning
			Architecture of SeqGAN
		Creating your own vocabulary for training
	Speech quality enhancement with SEGAN
		SEGAN architecture
		Training SEGAN to enhance speech quality
	Summary
	Further reading
Chapter 11: Reconstructing 3D models with GANs
	Fundamental concepts in computer graphics
		Representation of 3D objects
			Attributes of a 3D object
		Camera and projection
	Designing GANs for 3D data synthesis
		Generators and discriminators in 3D-GAN
		Training 3D-GAN
	Summary
	Further reading
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Index