What is a generative model?

A generative model is designed to learn the underlying distributions of data and use this learned information to create new data samples that follow the same distribution. This ability makes generative models particularly powerful for tasks like generating realistic images, text, or other forms of data. Unlike models that try to replicate specific existing data points, a generative model captures the essence of the data it has been trained on and can generate new examples that share similar features and statistical properties.

The other options do not accurately represent the purpose of generative models. For instance, the idea of mimicking existing data perfectly or recreating training data exactly is more aligned with descriptive models, which focus on accurately reflecting the input data without creating new instances. Furthermore, deterministic outputs imply that a model provides the same output for a particular input each time, which is not characteristic of generative models that may produce varied and novel outputs based on learned distributions.