A GAN that learns the Brenier potential using cubic-activation neural networks and a convexity penalty is shown to be statistically consistent, so the Jensen-Shannon distance from the generated to the target distribution can be made arbitrarily small.
Deep generative modelling: A comparative review of VAEs, GANs, normalizing flows, energy-based and autoregressive models,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Learning Brenier Potentials with Convex Generative Adversarial Neural Networks
A GAN that learns the Brenier potential using cubic-activation neural networks and a convexity penalty is shown to be statistically consistent, so the Jensen-Shannon distance from the generated to the target distribution can be made arbitrarily small.