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https://github.com/saadsalmanakram/gangineering
This repository covers the fundamentals of GAN architecture, including the generator and discriminator, with hands-on examples and code to help you grasp the intricacies of this powerful deep learning technique. Perfect for anyone looking to explore the creative and technical aspects of GANs.
https://github.com/saadsalmanakram/gangineering
generative-adversarial-network generative-adversarial-networks generative-ai
Last synced: about 13 hours ago
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This repository covers the fundamentals of GAN architecture, including the generator and discriminator, with hands-on examples and code to help you grasp the intricacies of this powerful deep learning technique. Perfect for anyone looking to explore the creative and technical aspects of GANs.
- Host: GitHub
- URL: https://github.com/saadsalmanakram/gangineering
- Owner: saadsalmanakram
- Created: 2024-08-06T15:56:22.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-01-28T20:28:29.000Z (17 days ago)
- Last Synced: 2025-01-28T21:28:14.821Z (17 days ago)
- Topics: generative-adversarial-network, generative-adversarial-networks, generative-ai
- Homepage:
- Size: 22.5 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
---
# 🧠 GANgineering: A Comprehensive Guide to Generative Adversarial Networks (GANs)
Welcome to **GANgineering**, your ultimate resource for mastering **Generative Adversarial Networks (GANs)**! This repository provides a deep dive into GANs, covering everything from basic principles to advanced architectures, training techniques, and applications.
## 🚀 What You'll Learn
- **🧑🏫 GAN Fundamentals:** Architecture, Training Dynamics, Loss Functions.
- **⚡ Advanced GAN Architectures:** DCGAN, WGAN, StyleGAN, CycleGAN, and more.
- **🛠️ Implementing GANs:** Practical implementation with PyTorch and TensorFlow.
- **🔬 GAN Applications:** Image Generation, Super-Resolution, Data Augmentation.
- **💡 GAN Optimization:** Training stability, mode collapse, regularization.
## 📌 Topics Covered
### 1️⃣ Introduction to GANs
- **What is a GAN?** Understanding the Generator and Discriminator.
- **Training Process:** Minimax Game and the Loss Function.
- **The Original GAN Paper** by Ian Goodfellow et al.
### 2️⃣ Basic GAN Architectures
- **Vanilla GAN (Basic Architecture)**
- **Deep Convolutional GAN (DCGAN)**
- **Conditional GAN (CGAN)**
### 3️⃣ Advanced GAN Architectures
- **Wasserstein GAN (WGAN)** and **WGAN-GP**
- **CycleGAN** for Image-to-Image Translation
- **StyleGAN & StyleGAN2** for High-Quality Image Synthesis
- **BigGAN** for Large Scale Image Generation
### 4️⃣ GAN Training Strategies
- **Avoiding Mode Collapse**
- **Improving Stability with Techniques like Spectral Normalization**
- **Adaptive Learning Rates with Adam Optimizer**
- **Progressive Training and Curriculum Learning**
### 5️⃣ GAN Applications
- **Image Generation** (Realistic Images from Noise)
- **Image Super-Resolution** (Enhancing Low-Resolution Images)
- **Data Augmentation** using GANs for AI Model Training
- **Image-to-Image Translation** (CycleGAN for style transfer)
### 6️⃣ Advanced GAN Topics
- **GAN Evaluation Metrics** (Inception Score, FID, IS)
- **Semi-Supervised Learning with GANs**
- **GANs in Video Generation**
- **Conditional GANs and Text-to-Image Generation**
### 7️⃣ Optimization Techniques
- **Improving Training with Regularization**
- **Exploring Different Loss Functions**
- **Network Initialization and Hyperparameter Tuning**
## 🛠️ Tech Stack
- **Python (PyTorch, TensorFlow, Keras)**
- **CUDA (for GPU Acceleration)**
- **OpenCV for Image Processing**
- **Matplotlib & Seaborn for Visualizations**
## 📌 Getting Started
1. **Clone the repository**
```bash
git clone https://github.com/saadsalmanakram/GANgineering.git
cd GANgineering
```
2. **Install dependencies**
```bash
pip install -r requirements.txt
```
3. **Run Example Code**
```bash
python train_dcgan.py # Example of training a DCGAN model
python generate_images.py # Generate images using a trained GAN
```
4. **Explore Advanced GANs**
Check out the specific GAN implementations in the `advanced/` folder, such as WGAN and CycleGAN.
## 📚 Resources & References
- [Original GAN Paper by Ian Goodfellow](https://arxiv.org/abs/1406.2661)
- [DCGAN Paper](https://arxiv.org/abs/1511.06434)
- [Wasserstein GAN Paper](https://arxiv.org/abs/1701.07875)
- [CycleGAN Paper](https://arxiv.org/abs/1703.10593)
- [StyleGAN Paper](https://arxiv.org/abs/1812.04948)
## 🤝 Contributing
Contributions are always welcome! If you have improvements or new GAN architectures to share, feel free to submit a pull request.
## ⭐ Support the Project
If you find this repository helpful, give it a ⭐ and share it with your network!
---