3 Generative Adversarial Networks (GANs)

 

This chapter covers

  • The fundamental concepts and architecture of Generative Adversarial Networks (GANs).
  • Key challenges in traditional GANs, including mode collapse, vanishing gradients, and non-convergence.
  • Evolution to Wasserstein GANs (WGANs), and how they address traditional GAN challenges.

In this chapter, following our discussion of Variational Autoencoders (VAEs), we continue our exploration of generative models by discussing Generative Adversarial Networks (GANs). We begin by unpacking the foundational principles of GANs, explaining their unique adversarial framework, and then explore the challenges that have spurred further innovation in this domain. A significant focus is placed on the evolution and implementation of Wasserstein GANs (WGANs), which have emerged as a powerful solution to overcome inherent limitations of traditional GANs. This chapter aims to provide a succinct yet comprehensive introduction to adversarial training, highlighting its significance and potential in the broader landscape of generative AI.

3.1 Introduction to GANs

Generative Adversarial Networks (GANs) represent one of the most influential advancements in generative AI, particularly in the context of image generation and processing. Developed in 2014 by Ian Goodfellow and his colleagues, [1] GANs have transformed the landscape of how machines interpret and create visual data, presenting new possibilities that were previously unattainable.

3.1.1 Adversarial Training

3.2 Core Concepts and Theoretical Foundations of GANs

3.2.1 Inspiration from Game Theory

3.2.2 GAN Value Function

3.2.3 Minimax Game: How the Value Function Works in GAN Training

3.2.4 Visualizing GAN Model Architecture

3.2.5 Reaching Equilibrium in GAN Training

3.3 Training Challenges in GANs

3.3.1 Mode Collapse

3.3.2 Vanishing Gradient

3.3.3 Non-Convergence

3.4 Wasserstein GANs: A Solution to Training Challenges

3.4.1 The Intuition Behind WGANs

3.4.2 Understanding Earth Mover’s (Wasserstein) Distance in WGANs

3.4.3 Replacing the Discriminator with a Critic

3.4.4 The WGAN Value Function

3.4.5 Visualizing WGAN Model Architecture

3.5 Implementing a WGAN in PyTorch

3.5.1 Adopting Best Practices from DCGANs

3.5.2 Step 1: Import Necessary Libraries

3.5.3 Step 2: Enable GPU Training

3.5.4 Step 3: Load the Dataset

3.5.5 Step 4: Implement the Generator

3.5.6 Step 5: Implement the Critic

3.5.7 Step 6: Initialize the Models and Optimizers

3.5.8 Step 7: Train the Model

3.5.9 Step 8: Evaluate the Model

3.5.10 Exploring the WGAN Latent Space

3.6 Conclusion

3.7 Summary