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Gen AI - Image Generation using GAN
Gen AI is a project focused on generating CIFAR-10-like images using Generative Adversarial Networks (GANs). The custom GAN model consists of a Generator and a Discriminator, trained to produce realistic images and differentiate between real and generated ones.
Table of Contents
Key Features
- Trains a custom GAN model on the CIFAR-10 dataset.
- Generates images resembling CIFAR-10 images with unique variations.
- Supports training and image generation via Jupyter Notebooks or Google Colab.
Use Case
- Data Augmentation: Augment existing datasets with synthetic images.
- Concept Exploration: Explore novel concepts and artistic expressions.
- Prototyping: Create mock-ups and test interfaces with generated images.
Usage
Setup
- Clone the repository to your local machine.
- Ensure dependencies are installed, including Python, TensorFlow, and Keras.
Training
- Open the provided Jupyter Notebook or upload it to Google Colab.
- Run the notebook to train the GAN model.
- Utilize GPU acceleration for faster training, especially on Google Colab.
Generation
- After training, use the trained model to generate new images.
- Experiment with different latent space values to explore image variations.
Note
- No graphical user interface (UI) is provided. Interact with the project through Jupyter Notebooks or Google Colab.
- GPU acceleration is recommended for efficient training.
- Contributions are welcome! Fork the repository, make improvements, and submit pull requests.
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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.
38/100 healthActive repository
Jupyter NotebookNo license#deep-learning#deep-neural-networks#generative-adversarial-network#generative-ai
⑂ 0 forks◯ 0 issuesUpdated Sep 22, 2025