Normalizing flows

How it trains

Maximum likelihood is optimized exactly under architectural constraints that make inversion and the Jacobian determinant tractable.

How inference runs

Density evaluation runs data toward the latent; generation samples the base distribution and applies every transform in reverse.

Strengths

• •Exact likelihood under the model
• •Invertible encoding and generation
• •Useful when density estimation is itself important

Trade-offs

• •Invertibility constrains network design
• •High-dimensional media can require deep, memory-heavy flows
• •Likelihood does not necessarily track perceived sample quality

Use it when

• Exact density or reversible transforms are requirements
• The domain fits available invertible architectures
• You will evaluate both likelihood and task utility

Avoid or challenge it when

• Only perceptual generation quality matters
• Architectural flexibility is more important than exact likelihood
• A simpler discriminative uncertainty method is sufficient