Learning generative adversarial networks _ next-generation deep learning simplified_Ganguly, Kuntal.pdf

Table of Contents Learning Generative Adversarial Networks Credits About the Author About the Reviewer www.PacktPub.com eBooks, discount offers, and more Why subscribe? 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 CycleGA