A generative model is a model that learns the distribution or structure of training data in order to create new examples or estimate how likely examples are. It can generate content, simulate data, or support tasks such as representation learning and anomaly detection.
Practically speaking, a model that does either of the following: - Creates (generates) new examples from the training dataset. For example, a generative model could create poetry after training on a dataset of poems. The generator part of a generative adversarial network falls into this category. - Determines the probability that a new example comes from the training set, or was created from the same mechanism that created the training set. For example, after training on a dataset consisting of English sentences, a generative model could determine the probability that new input is a valid English sentence. A generative model can theoretically discern the distribution of examples or particular features in a dataset. That is: Unsupervised learning models are generative. Contrast with discriminative models.
A type of machine learning model that can create novel outputs based on its training data. At its simplest, the model generates new data that looks like a certain set of categories that it was trained on. Usually associated with large language models, but other types of models can be generative as well.