General Proximal Flow Networks

Researchers introduce General Proximal Flow Networks (GPFNs), a generalization of Bayesian Flow Networks that allows for arbitrary divergence functions instead of fixed Kullback-Leibler divergence. The framework enables iterative generative modeling with improved generation quality when divergence functions are adapted to underlying data geometry.