Adversarial network training using higher-order moments in a modified Wasserstein distance
Machine Learning
2022-10-10 v1 Machine Learning
Abstract
Generative-adversarial networks (GANs) have been used to produce data closely resembling example data in a compressed, latent space that is close to sufficient for reconstruction in the original vector space. The Wasserstein metric has been used as an alternative to binary cross-entropy, producing more numerically stable GANs with greater mode covering behavior. Here, a generalization of the Wasserstein distance, using higher-order moments than the mean, is derived. Training a GAN with this higher-order Wasserstein metric is demonstrated to exhibit superior performance, even when adjusted for slightly higher computational cost. This is illustrated generating synthetic antibody sequences.
Cite
@article{arxiv.2210.03354,
title = {Adversarial network training using higher-order moments in a modified Wasserstein distance},
author = {Oliver Serang},
journal= {arXiv preprint arXiv:2210.03354},
year = {2022}
}