Epistemic Generative Adversarial Networks

ICLR 2026 Conference Submission19859 Authors

Keywords: Generative Adversarial Networks, Uncertainty Quantification, Computer Vision, Deep Generative Models, Deep Learning

TL;DR: Enabling more diverse generations through uncertainty aware generative models

Abstract: Generative models, particularly Generative Adversarial Networks (GANs), often suffer from a lack of output diversity, frequently generating similar samples rather than a wide range of variations. This paper introduces a novel generalization of the GAN loss function based on Dempster-Shafer theory of evidence, applied to both the generator and discriminator. Additionally, we propose an architectural enhancement to the generator that enables it to predict a mass function for each image pixel. This modification allows the model to quantify uncertainty in its outputs and leverage this uncertainty to produce more diverse and representative generations. Experimental evidence shows that our approach not only improves generation variability but also provides a principled framework for modeling and interpreting uncertainty in generative processes.

Primary Area: generative models

Submission Number: 19859