This repository contains a Generative Adversarial Network (GAN) implemented in TensorFlow using PyTorch. The GAN is capable of generating synthetic images for any given dataset. The architecture consists of a discriminator model responsible for distinguishing between real and fake images, and a generator model tasked with generating realistic-looking synthetic images.
You can use your own dataset for training the GAN. Ensure the dataset is preprocessed, and images are converted into tensors and normalized.
After training, you can visualize both real and generated images to assess the training progress qualitatively.
The trained discriminator and generator models are saved for future use, allowing you to generate synthetic images without retraining.
Using the pretrained models, you can generate synthetic images by running the appropriate script.