Continuous normalizing flows are a powerful class of generative models that transform a simple distribution into a complex one via a series of invertible transformations. Each transformation is defined by a close function that preserves the closeness between the input and output distributions, and its Jacobian determinant quantifies the change in volume induced by the transformation.
