architectures

GAN

Generative Adversarial Network, an architecture consisting of two neural networks: a generator that creates synthetic data and a discriminator that tries to distinguish real from generated data. The two networks train adversarially, pushing each other to improve.

In practice

StyleGAN can generate photorealistic human faces that are indistinguishable from real photographs.

Generative AI
AI systems capable of creating new content including text, images, audio, video, and code. Generative AI models learn the underlying patterns and structure of training data, then produce novel outputs that follow similar patterns.
Diffusion Model
A generative model that learns to create data by reversing a gradual noising process. During training, noise is progressively added to data; the model learns to denoise step by step. At generation time, it starts from pure noise and iteratively refines it into coherent output.
VAE
Variational Autoencoder, a generative model that learns a compressed latent representation of data and can generate new samples by decoding points from this latent space. VAEs combine autoencoders with probabilistic modeling to enable controlled generation.