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🖼️ Gen AI – Image Generation using GAN
A Generative AI project focused on creating CIFAR-10–like images using Generative Adversarial Networks (GANs).
The custom model consists of a Generator and a Discriminator, trained end-to-end to produce realistic images and distinguish them from real ones.
🚀 Key Features
- Train a custom GAN model on the CIFAR-10 dataset.
- Generate synthetic images resembling CIFAR-10 with unique variations.
- Run training and generation seamlessly on Jupyter Notebooks or Google Colab (GPU-accelerated).
🎯 Use Cases
- Data Augmentation: Enhance datasets with synthetic images.
- Concept Exploration: Experiment with new concepts and artistic expressions.
- Rapid Prototyping: Create mock-ups and test interfaces using generated content.
🏷️ Tech Stack
Generative AI · GANs · Deep Learning · Image Generation · CIFAR-10 · Python · TensorFlow/Keras
⚙️ Getting Started
1️⃣ Setup
- Clone the repository.
- Install Python dependencies:
TensorFlow, Keras, and other requirements in requirements.txt.
2️⃣ Training
- Open the Jupyter Notebook locally or upload it to Google Colab.
- Run all cells to train the GAN model.
- Use GPU acceleration (highly recommended).
3️⃣ Image Generation
- After training, use the trained model to generate new images.
- Experiment with latent space parameters to explore diverse variations.
📝 Notes
- No graphical user interface (GUI) is provided — interaction is via notebooks/Colab.
- GPU acceleration significantly improves training speed.
- Contributions welcome! Fork, enhance, and open pull requests.
📸 Screenshots
| Training Progress | Generated Images |
|---|
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Gen AI uses GANs to generate CIFAR-10-like images. The custom GAN model comprises a Generator and a Discriminator. Users can train the model and generate images using Jupyter Notebooks or Google Colab.
60/100 healthActive repository
Jupyter NotebookNo license#deep-learning#deep-neural-networks#deeplearning#generative-adversarial-network
⑂ 10 forks◯ 0 issuesUpdated May 23, 2026