Leverage Score Sampling for Complete Mode Coverage in Generative Adversarial Networks

TL;DR

This paper introduces a ridge leverage score-based sampling method to enhance mode coverage in GANs, addressing the common issue of underrepresented data modes in generative modeling, and demonstrates its effectiveness through multiple evaluations.

Contribution

It proposes a novel leverage score sampling technique that improves mode coverage in GANs, compatible with existing models and leveraging explicit or implicit feature maps.

Findings

Significant improvement in mode coverage over standard methods

Effective when combined with any GAN architecture

Validated through multiple comparative evaluations

Abstract

Commonly, machine learning models minimize an empirical expectation. As a result, the trained models typically perform well for the majority of the data but the performance may deteriorate in less dense regions of the dataset. This issue also arises in generative modeling. A generative model may overlook underrepresented modes that are less frequent in the empirical data distribution. This problem is known as complete mode coverage. We propose a sampling procedure based on ridge leverage scores which significantly improves mode coverage when compared to standard methods and can easily be combined with any GAN. Ridge leverage scores are computed by using an explicit feature map, associated with the next-to-last layer of a GAN discriminator or of a pre-trained network, or by using an implicit feature map corresponding to a Gaussian kernel. Multiple evaluations against recent approaches of complete mode coverage show a clear improvement when using the proposed sampling strategy.