Generative model

Topic

A generative model is a class of machine learning models designed to capture the underlying probability distribution of a dataset, allowing it to generate new, synthetic data points that resemble the training data. Often trained using unsupervised learning, these models estimate the joint probability distribution of inputs and outputs to describe the full data-generating process. Common examples include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models.