Flow-based Deep Generative Models

Lilian Weng · 7 years ago

Flow-based generative models use normalizing flows to explicitly learn the probability density function of data by applying sequences of invertible transformations, addressing a limitation of GANs and VAEs which cannot directly calculate this density. RealNVP implements this approach using affine coupling layers that split input dimensions and apply scale-and-shift transformations, with Jacobian determinants that are computationally tractable. This enables training via negative log-likelihood loss, allowing the models to generate samples, estimate densities, and perform inference on incomplete data more effectively than prior generative model architectures.