generative model

A generative model is a statistical or neural model that attempts to learn or approximate the underlying probability distribution of data. A good generative model can typically:

1. Generate new samples consistent with that distribution
2. Assign likelihoods or densities for observed data points

Modern generative techniques include autoregressive models, variational autoencoders (VAEs), normalizing flows, diffusion and score-based models, and generative adversarial networks (GANs).

These generative models are trained using objectives designed to make their learned distribution approximate the true data distribution—such as through maximum likelihood, adversarial losses, score matching, or diffusion-based estimation.

The end goal is that one can either sample from the model and evaluate, or approximate densities or log-likelihoods.

Related Resources

Tutorial

Generative Adversarial Networks: Build Your First Models

In this step-by-step tutorial, you'll learn all about one of the most exciting areas of research in the field of machine learning: generative adversarial networks. You'll learn the basics of how GANs are structured and trained before implementing your own generative model using PyTorch.

advanced machine-learning