Generative Adversarial Networks (GANs)

The Adversarial Game

GANs revolutionized generative modeling by framing generation as a game between two neural networks: a Generator that creates fake data and a Discriminator that tries to distinguish real from fake. This adversarial process pushes both networks to improve, resulting in remarkably realistic generations.

GAN Training Process

Watch how the generator learns to create realistic samples while the discriminator learns to distinguish real from fake.

Epoch: 0 / 100

Generator Strategy

Create samples that maximize D(G(z)), fooling the discriminator into thinking they're real.

Discriminator Strategy

Correctly classify real samples as real (output → 1) and fake samples as fake (output → 0).

How GANs Work

The Generator

Takes random noise as input and transforms it into data that resembles the training distribution. It learns to fool the discriminator by producing increasingly realistic samples.

The Discriminator

A binary classifier that learns to distinguish between real data from the training set and fake data produced by the generator. It outputs a probability that the input is real.

The Training Process

  1. Train discriminator on real and fake samples
  2. Train generator to fool the discriminator
  3. Repeat until equilibrium is reached

Loss Functions

The GAN objective is a minimax game:

min_G max_D V(D,G) = E[log D(x)] + E[log(1 - D(G(z)))]
  • Discriminator loss: Maximize probability of correct classification
  • Generator loss: Minimize log(1 - D(G(z))) or maximize log(D(G(z)))

Latent Space to Generation

Adjust the latent vector components to see how they affect the generated pattern. Notice how smooth changes in latent space create smooth changes in output.

Key Insights:

  • • Each dimension in the latent space controls different aspects
  • • Smooth interpolation in latent space = smooth changes in output
  • • The generator learns to map this space to realistic data
  • • Random sampling from this space creates diverse outputs

Common Challenges

1. Mode Collapse

The generator finds a few samples that fool the discriminator and keeps producing only those, ignoring the diversity of the real data distribution.

2. Training Instability

The adversarial nature makes training unstable. If one network becomes too strong, the other stops learning effectively.

3. Vanishing Gradients

When the discriminator is too good, the generator gradients vanish, preventing learning.

GAN Variants

DCGAN (Deep Convolutional GAN)

Uses convolutional layers and specific architectural guidelines for stable training.

WGAN (Wasserstein GAN)

Uses Wasserstein distance instead of JS divergence, providing better gradients.

StyleGAN

Introduces style transfer concepts for high-quality, controllable generation.

CycleGAN

Enables unpaired image-to-image translation between domains.

Why GANs Matter

  • High-Quality Generation: Produce the most realistic synthetic data
  • Unsupervised Learning: Learn features without labels
  • Creative Applications: Art generation, style transfer, super-resolution
  • Data Augmentation: Generate training data for other models
  • Scientific Discovery: Drug discovery, material design