Learning Generative Adversarial Networks by Langr, Jakub -- Read -- Imperial Library of Trantor

Index

Learning Generative Adversarial Networks

Table of Contents Learning Generative Adversarial Networks Credits About the Author About the Reviewer www.PacktPub.com

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Customer Feedback Preface

What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support

Downloading the example code Downloading the color images of this book Errata Piracy Questions

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

Index

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