Flow models

Flow models are a type of machine learning technique used to generate data by learning how to reverse complex data transformations. Imagine they learn a series of reversible steps that can turn random noise into realistic images, voices, or other data types. Once trained, these models can create new, high-quality data by reversing these steps, making them powerful for tasks like image synthesis and data augmentation. They are valued for their ability to model complex distributions precisely while allowing direct, efficient sampling of new data.