a GAN from Scratch in PyTorch to Generate MNIST Digits

by dev

Generative Adversarial Networks (GANs) have revolutionized the field of generative modeling, enabling us to create realistic images, art, and even deepfakes.

In this blog, we'll walk through building a simple GAN in PyTorch to generate images of handwritten digits from the MNIST dataset.

We'll cover the architecture, training loop, and important implementation details, including a crucial fix for stable training.

Introduction to GANs

A GAN consists of two neural networks:

• Generator (G): Learns to create fake images that resemble real data.
• Discriminator (D): Learns to distinguish between real images (from the dataset) and fake images (from the generator).

These two networks are trained in a competitive setting: the generator tries to fool the discriminator, while the discriminator tries not to be fooled.