AI engineering glossary · Generative media multimodal
What is normalizing flow?
Normalizing flow: A generative model built from a chain of invertible transformations that turn a simple distribution such as a Gaussian into a complex data distribution. Because each step is reversible with a known determinant, it gives exact likelihoods and exact sampling.
Normalizing flow: an example
A flow of 16 invertible layers maps Gaussian noise to a plausible face and can map any face back to noise.
Related terms
- Variational autoencoder
- Energy-based model
- Generative adversarial network
More Generative media multimodal terms
Where you learn it
Normalizing flow is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.