Normalizing Flows
Normalizing flows are a class of generative models in machine learning that transform a simple probability distribution (e.g., Gaussian) into a complex target distribution through a series of invertible and differentiable transformations. They enable exact likelihood computation and efficient sampling, making them useful for density estimation and data generation tasks. By leveraging the change-of-variables formula, they model data distributions with high flexibility and tractability.
Developers should learn normalizing flows when working on tasks requiring precise density estimation, such as anomaly detection, Bayesian inference, or generating high-quality synthetic data in fields like image synthesis or molecular design. They are particularly valuable in scenarios where exact likelihoods are needed, unlike GANs which lack this property, and offer advantages over VAEs in terms of more flexible latent representations.