WGAN-GP on MNIST

This project implements a Wasserstein GAN with Gradient Penalty (WGAN-GP) to generate handwritten digits using the MNIST dataset.

Features

• Generator and Critic (Discriminator) networks built with PyTorch.
• Gradient Penalty for improved training stability.
• Image generation using 100-dim latent vectors.
• Training progress visualized at selected epochs.