PAC-Bayesian Generalization Bounds for Adversarial Generative Models
Machine Learning
2023-11-15 v4 Artificial Intelligence
Machine Learning
Abstract
We extend PAC-Bayesian theory to generative models and develop generalization bounds for models based on the Wasserstein distance and the total variation distance. Our first result on the Wasserstein distance assumes the instance space is bounded, while our second result takes advantage of dimensionality reduction. Our results naturally apply to Wasserstein GANs and Energy-Based GANs, and our bounds provide new training objectives for these two. Although our work is mainly theoretical, we perform numerical experiments showing non-vacuous generalization bounds for Wasserstein GANs on synthetic datasets.
Keywords
Cite
@article{arxiv.2302.08942,
title = {PAC-Bayesian Generalization Bounds for Adversarial Generative Models},
author = {Sokhna Diarra Mbacke and Florence Clerc and Pascal Germain},
journal= {arXiv preprint arXiv:2302.08942},
year = {2023}
}
Comments
Published at ICML 2023