Generative Adversarial Networks (GAN) Project

This project focuses on building, training, and testing a Generative Adversarial Network (GAN) using PyTorch. The goal is to generate high-quality, visually convincing images while addressing common GAN training challenges.

Project Overview

The project leverages GANs for image generation tasks, utilizing datasets such as CelebA and AnimeFace. Key objectives include designing a Deep Convolutional GAN (DCGAN) architecture, stabilizing GAN training, and exploring transfer learning techniques.

Key Features

• Model Implementation:

  • Designed and implemented a DCGAN architecture in PyTorch.
  • Trained models to generate realistic images from latent vectors.

• Transfer Learning:

  • Fine-tuned pre-trained GAN models to adapt to domain-specific tasks, such as generating AnimeFace images.

• Training Stability:

  • Addressed challenges in GAN training, including balancing generator and discriminator convergence.
  • Experimented with hyperparameters to achieve stable and effective training.

Datasets

• CelebA Dataset: A large-scale face attributes dataset used for generating realistic human faces.
• AnimeFace Dataset: A domain-specific dataset for generating high-quality anime-style images.

Results

• Generated visually appealing and realistic images for both datasets.
• Demonstrated the effectiveness of DCGAN in handling complex data distributions.
• Explored the potential of transfer learning in improving GAN performance.

Tools and Technologies

• PyTorch: For building and training the GAN models.
• Matplotlib: For visualizing training progress and generated images.
• Google Colab: Used as the development environment for its GPU support.