Learning generative adversarial networks : next-generation deep learning simplified

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Book Description

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Build image generation and semi-supervised models using Generative Adversarial Networks

About This Book

Understand the buzz surrounding Generative Adversarial Networks and how they work, in the simplest manner possible

Develop generative models for a variety of real-world use-cases and deploy them to production

Contains intuitive examples and real-world cases to put the theoretical concepts explained in this book to practical use

Who This Book Is For

Data scientists and machine learning practitioners who wish to understand the fundamentals of generative models will find this book useful. Those who wish to implement Generative Adversarial Networks and their variant architectures through real-world examples will also benefit from this book. No prior knowledge of generative models or GANs is expected.

What You Will Learn

Understand the basics of deep learning and the difference between discriminative and generative models

Generate images and build semi-supervised models using Generative Adversarial Networks (GANs) with real-world datasets

Tune GAN models by addressing the challenges such as mode collapse, training instability using mini batch, feature matching, and the boundary equilibrium technique.

Use stacking with Deep Learning architectures to run and generate images from text.

Couple multiple Generative models to discover relationships across various domains

Explore the real-world steps to deploy deep models in production

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Table of Contents

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1. Introduction to Deep Learning

Evolution of deep learning

Sigmoid activation

Rectified Linear Unit (ReLU)

Exponential Linear Unit (ELU)

Stochastic Gradient Descent (SGD)

Learning rate tuning

Regularization

Shared weights and pooling

Local receptive field

Convolutional network (ConvNet)

Deconvolution or transpose convolution

Recurrent Neural Networks and LSTM

Deep neural networks

Discriminative versus generative models

Summary

2. Unsupervised Learning with GAN

Automating human tasks with deep neural networks

The purpose of GAN

An analogy from the real world

The building blocks of GAN

Generator

Discriminator

Implementation of GAN

Applications of GAN

Image generation with DCGAN using Keras

Implementing SSGAN using TensorFlow

Setting up the environment

Challenges of GAN models

Setting up failure and bad initialization

Mode collapse

Problems with counting

Problems with perspective

Problems with global structures

Improved training approaches and tips for GAN

Feature matching

Mini batch

Historical averaging

One-sided label smoothing

Normalizing the inputs

Batch norm

Avoiding sparse gradients with ReLU, MaxPool

Optimizer and noise

Don't balance loss through statistics only

Summary

3. Transfer Image Style Across Various Domains

Bridging the gap between supervised and unsupervised learning

Introduction to Conditional GAN

Generating a fashion wardrobe with CGAN

Stabilizing training with Boundary Equilibrium GAN

The training procedure of BEGAN

Architecture of BEGAN

Implementation of BEGAN using Tensorflow

Image to image style transfer with CycleGAN

Model formulation of CycleGAN

Transforming apples into oranges using Tensorflow

Transfiguration of a horse into a zebra with CycleGAN

Summary

4. Building Realistic Images from Your Text

Introduction to StackGAN

Conditional augmentation

Stage-I

Stage-II

Architecture details of StackGAN

Synthesizing images from text with TensorFlow

Discovering cross-domain relationships with DiscoGAN

The architecture and model formulation of DiscoGAN

Implementation of DiscoGAN

Generating handbags from edges with PyTorch

Gender transformation using PyTorch

DiscoGAN versus CycleGAN

Summary

5. Using Various Generative Models to Generate Images

Introduction to Transfer Learning

The purpose of Transfer Learning

Various approaches of using pre-trained models

Classifying car vs cat vs dog vs flower using Keras

Large scale deep learning with Apache Spark

Running pre-trained models using Spark deep learning

Handwritten digit recognition at a large scale using BigDL

High resolution image generation using SRGAN

Architecture of the SRGAN

Generating artistic hallucinated images using DeepDream

Generating handwritten digits with VAE using TensorFlow

A real world analogy of VAE

A comparison of two generative models—GAN and VAE

Summary

6. Taking Machine Learning to Production

Building an image correction system using DCGAN

Steps for building an image correction system

Challenges of deploying models to production

Microservice architecture using containers

Drawbacks of monolithic architecture

Benefits of microservice architecture

Containers

Docker

Kubernetes

Benefits of using containers

Various approaches to deploying deep models

Approach 1 - offline modeling and microservice-based containerized deployment

Approach 2 - offline modeling and serverless deployment

Approach 3 - online learning

Approach 4 - using a managed machine learning service

Serving Keras-based deep models on Docker

Deploying a deep model on the cloud with GKE

Serverless image recognition with audio using AWS Lambda and Polly

Steps to modify code and packages for lambda environments

Running face detection with a cloud managed service

Summary