Generative Adversarial Networks Specialization (GANs) Solution

This repository contains my solutions for the Coursera course Generative Adversarial Networks Specialization (GANs).

The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs.

Note: Incase any of the notebooks do not load on Github, copy the link of the .ipynb file and paste on this site https://nbviewer.jupyter.org/

Course 1: Build Basic Generative Adversarial Networks Specialization (GANs)

The fundamental components of GANs, build a basic GAN using PyTorch, use convolutional layers to build advanced DCGANs that processes images, apply W-Loss function to solve the vanishing gradient problem, and learn how to effectively control your GANs and build conditional GANs.

• Week 1:
  • Your first GANs
• Week 2:
  • Deep Convolutional GANs
• Week 3:
  • WGAN
• Week 4:
  • Build a Conditional GAN
  • Controllable Generation

Course 2: Build Better Generative Adversarial Networks (GANs)

The challenges of evaluating GANs, compare different generative models, use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs, identify sources of bias and the ways to detect it in GANs, and learn and implement the techniques associated with the state-of-the-art StyleGAN.

• Week 1:
  • Frechet Inception Distance
• Week 2:
  • Bias
• Week 3:
  • Components of StyleGAN

Course 3: Apply Generative Adversarial Networks (GANs)

Use GANs for data augmentation and privacy preservation, survey more applications of GANs, and build Pix2Pix and CycleGAN for image translation.

• Week 1:
  • Data Augmentation
• Week 2:
  • U-Net
  • Pix2Pix
• Week 3:
  • CycleGAN