Description: The normalizing flow is a generative model that transforms a simple distribution into a more complex one through a series of invertible transformations. This approach is based on the idea that it is possible to map data from a simple distribution, such as a normal distribution, to a more complicated distribution that better represents the structure of real data. The transformations are designed to be invertible, meaning that the original distribution can be recovered from the complex one. This process allows models to learn rich and detailed representations of data, facilitating the generation of new examples that are consistent with the learned distribution. Normalizing flows are particularly useful in tasks such as generative modeling for various types of data, including images, audio, and other high-dimensional data, where the complexity of the data requires a more sophisticated approach than traditional generative models. Additionally, their ability to perform efficient inference and sampling makes them valuable tools in the fields of machine learning and artificial intelligence, where the quality and diversity of generated data are crucial.
