Learning normalized image densities via dual score matching

Understand

Learning probability models from data is at the heart of many machine learning endeavors, but is notoriously difficult due to the curse of dimensionality.

• We introduce a new framework for learning \emph{normalized} energy (log probability) models that is inspired by diffusion generative models, which rely on networks optimized to estimate the score.
• We modify a score network architecture to compute an energy while preserving its inductive biases.
• The gradient of this energy network with respect to its input image is the score of the learned density, which can be optimized using a denoising objective.