Normalizing flows

Entraînement

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

Exécution de l’inférence

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

Atouts

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

Compromis

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

À utiliser lorsque

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

À éviter ou remettre en question lorsque

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