Different GAN Architectures

Overview

Generative Adversarial Networks (GANs) have revolutionized the field of artificial intelligence and machine learning, particularly in the domain of image synthesis and processing. This project aims to deeply understand the fundamentals of GAN algorithms and contribute to their development by examining various GAN architectures and comparing their results.

Objectives

• To investigate different GAN algorithm architectures.
• To compare the performance and outcomes of these architectures.
• To contribute to the understanding and advancement of GAN models.

Methodology

1. GAN Architectures Covered

• DCGAN: Deep Convolutional GAN
• WGAN: Wasserstein GAN
• WGAN-GP: WGAN with Gradient Penalty
• LSGAN: Least Squares GAN
• CycleGAN: Cycle-Consistent Adversarial Network
• StyleGAN: Style-Based GAN

2. Evaluation Metrics

The models were evaluated based on visual quality, convergence speed, and quantitative measures such as Inception Score and FID (Frechet Inception Distance).