Adversarial network training using higher-order moments in a modified Wasserstein distance
This work addresses the challenge of more stable and effective GAN training for generating synthetic data, such as antibody sequences, but appears incremental as it builds on existing Wasserstein metric methods.
The authors tackled the problem of improving generative-adversarial networks (GANs) by deriving a generalization of the Wasserstein distance using higher-order moments, which demonstrated superior performance in generating synthetic antibody sequences.
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.