The Score-Difference Flow for Implicit Generative Modeling

Understand

Implicit generative modeling (IGM) aims to produce samples of synthetic data matching the characteristics of a target data distribution.

• Recent work (e.g.
• score-matching networks, diffusion models) has approached the IGM problem from the perspective of pushing synthetic source data toward the target distribution via dynamical perturbations or flows in the ambient space.
• In this direction, we present the score difference (SD) between arbitrary target and source distributions as a flow that optimally reduces the Kullback-Leibler divergence between them.