# Generative Adversarial Networks (GANs)


📂 model-architectures

## MODEL ARCHITECTURES

# Generative Adversarial Networks (GANs)

Train a generator to fool a discriminator while the discriminator learns to distinguish generated samples from training data.

## MENTAL MODEL

A forger and a critic improve against each other; after training, the forger can generate in one forward pass.

## DATA FLOW

- Random latent + condition

- Generator

- Synthetic sample

- Discriminator compares real vs synthetic

- Adversarial gradients update both

## How it trains

A minimax adversarial objective couples two networks. Practical variants change losses, regularization, normalization, and conditioning to stabilize training and control outputs.

## How inference runs

Sample a latent and run the generator once, making generation fast. Inversion or editing requires additional machinery because there is no built-in iterative reverse process.

## Strengths

- •
Fast one-pass sampling

- •
Sharp outputs in well-scoped visual domains

- •
Useful conditional and image-to-image variants

## Trade-offs

- •
Training instability and mode collapse

- •
Coverage and likelihood are hard to assess

- •
Large open-domain text-conditioned generation has shifted toward other families

## Use it when

- Low-latency sampling matters in a constrained domain

- A proven GAN pipeline already fits the data and controls

- Diversity and coverage are explicitly evaluated

## Avoid or challenge it when

- Stable training and broad mode coverage are primary requirements

- The team lacks domain-specific evaluation

- A newer diffusion or autoregressive baseline is not being compared

## Illustrative published families

- • Original GAN formulation

- • StyleGAN research family

- • Conditional image-to-image GANs

## Commonly combines with

## PRIMARY REFERENCES

## Model Architectures

## Model Architectures