Description: Distribution learning is an approach within generative models that focuses on understanding and modeling the underlying distribution of a dataset. This process involves identifying patterns and characteristics in existing data to generate new samples that are consistent with the original distribution. Through statistical techniques and machine learning algorithms, the goal is to capture the essence of the data, allowing not only the generation of new instances but also tasks such as data interpolation and extrapolation. Generative models, such as Generative Adversarial Networks (GANs) and Gaussian Mixture Models (GMMs), are examples of tools used for this purpose. The ability to learn distributions is fundamental in various applications, from image synthesis and music generation to text creation and data simulation in environments where data collection is costly or impractical. In summary, distribution learning enables artificial intelligence systems to not only understand the world through data but also to create new realities based on that understanding.
History: null
Uses: null
Examples: null
