Welcome to the Generative Adversarial Networks (GANs) section! This folder provides an introduction to GANs, a class of deep learning models used for generating realistic synthetic data. GANs consist of two neural networks, a Generator and a Discriminator, that compete in a game-theoretic framework to improve data generation quality.

Note: The notebooks here introduce foundational GAN concepts but do not cover all advanced variations. For a more comprehensive understanding, please refer to the recommended resources provided below.

This folder currently includes:

• PyTorch Implementation: A simple GAN trained on the Fashion MNIST dataset and GAN Architectures that explore the implementations of common GAN architectures in PyTorch.
• TensorFlow Implementation: A GAN-based model to generate realistic masked face images using various GAN architectures like Inception V2, Xception and others

Each section includes assignments to reinforce learning, along with solutions for self-assessment.

Follow these steps to build a strong foundation in GANs:

• Purpose: Train a simple GAN to generate realistic images of Fashion MNIST clothing items.
• Topics to Cover:
  • Generator and Discriminator networks
  • Training loop and adversarial loss
  • Evaluating GAN-generated images
• Resources:
  • Fashion MNIST Dataset
  • GANs Explained (Stanford CS231n)

• Purpose: Use Convolutional Neural Networks to implement a model that detects whether a person is wearing a mask.
• Topics to Cover:
  • CNN-based GAN training
  • Data augmentation with synthetic images
  • Evaluating model performance.
• Resources:
  • TensorFlow GANs Guide
  • Face Mask Dataset

Each GAN model includes hands-on assignments designed to help you apply what you've learned. Solutions are provided for self-evaluation. Try to complete the assignments independently before checking the solutions for the best learning experience.

1. Begin with Fashion MNIST GAN (PyTorch): Understand how basic GANs generate synthetic images.
2. Move to TensorFlow: Mask Detection GAN: Use GANs for augmenting masked face datasets.

Happy coding! Developing GANs will enable you to generate high-quality synthetic data and explore creative AI applications. For further learning, refer to the documentation and tutorials linked above.