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#