Auxiliary Classifier Generative Adversarial Network (ACGAN)

This folder contains an implementation of an Auxiliary Classifier Generative Adversarial Network (ACGAN) using PyTorch. ACGAN extends the traditional GAN architecture by incorporating class information into both the generator and discriminator, allowing control over the generated samples' characteristics.

Overview

ACGANs are capable of generating high-quality images conditioned on specific classes. In addition to generating images, the discriminator in ACGAN also predicts the class labels of the generated images. This allows for more controlled and targeted image synthesis.

Usage

To use this implementation, follow these steps:

Clone the repository:

git clone https://github.com/UTSAVS26/GAN-models.git
cd GAN_models/ACGAN

Install dependencies: Make sure you have Python 3 and pip installed. Then install the required dependencies:
```
pip install -r requirements.txt
```
This will install PyTorch, torchvision, matplotlib, and numpy.
Train the ACGAN: Run the ACGAN.py script to train the ACGAN model. This will train the ACGAN on the MNIST dataset and save the trained models (G_acgan.pth and D_acgan.pth).
```
python ACGAN.py
```
Generate new images: After training, you can generate new images using the trained generator by running the test_ACGAN.py script.
```
python test_ACGAN.py
```
This script loads the trained generator model and generates a grid of sample images.

Files

ACGAN.py: Contains the implementation of the ACGAN model, training loop, and saving of trained models.
test_ACGAN.py: Uses the trained generator to generate sample images after training.

Contributing

Contributions are welcome! If you have ideas for improvements or new features, feel free to open an issue or submit a pull request.

Author

Utsav Singhal