DL Book 6 - Generative Adversarial Networks for Image Synthesis for Absolute Beginners

Table of Contents

1. Introduction

1.1 What are Generative Adversarial Networks?

1.2 Why are GANs useful for image synthesis?

1.3 What are the challenges and limitations of GANs?

1.4 How to use this book?

2. Basic Concepts and Terminology

2.1 Generative Models and Density Estimation

2.2 Neural Networks and Deep Learning

2.3 Convolutional Neural Networks and Image Processing 2.4 Optimization and Loss Functions

3. The GAN Framework and Architecture

3.1 The GAN Game and Objective

3.2 The Generator and the Discriminator

3.3 The Training Procedure and Algorithm

3.4 The Evaluation Metrics and Criteria

4. Implementing GANs with Python and PyTorch

4.1 Setting up the Environment and Dependencies

4.2 Loading and Preprocessing the Data

4.3 Defining and Initializing the Models

4.4 Training and Testing the Models

4.5 Visualizing and Saving the Results

5. Exploring Different Types of GANs for Image Synthesis 5.1 DCGAN: Deep Convolutional GAN

5.2 CGAN: Conditional GAN

5.3 WGAN: Wasserstein GAN

5.4 CycleGAN: Cycle-Consistent GAN

5.5 StyleGAN: Style-Based GAN

6. Conclusion and Future Directions

6.1 Summary and Key Takeaways

6.2 Challenges and Open Problems

6.3 Resources and References