Normalizing Flow
Definition
Normalizing flows model distributions through invertible transformations.
A generative model composed of invertible transformations that can represent complex distributions with exactly computable density.
Simply explained
A simple numerical distribution is gradually deformed, with each step also reversible.
Why it matters in practice
Helps realistically assess model quality, limits, and technical decisions.
Example from business
A model learns a probability density for sensor data and can generate samples from it.
Limits and pitfalls
- Do not confuse with Flow Matching or omit invertibility and computational costs.
Origin & history
Normalizing Flows evolved from variable transformation and became widespread with NICE, RealNVP, and related models.
Use in mid-sized companies
- Possible application: A model learns a probability density for sensor data and can generate samples from it.
Frequently asked questions
What does Normalizing Flow mean?
A generative model composed of invertible transformations that can represent complex distributions with exactly computable density.
Which mistakes should be avoided?
Do not confuse with Flow Matching or omit invertibility and computational costs.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: