The Bures Metric for Generative Adversarial Networks

de Meulemeester, Hannes; Schreurs, Joachim; Fanuel, Michaël; de Moor, Bart; Suykens, Johan

Document type :

Partie d'ouvrage

DOI :

10.1007/978-3-030-86520-7_4

Title :

The Bures Metric for Generative Adversarial Networks

Author(s) :

de Meulemeester, Hannes [Auteur]
Schreurs, Joachim [Auteur]
Fanuel, Michaël [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
de Moor, Bart [Auteur]
Suykens, Johan [Auteur]

Book title :

Machine Learning and Knowledge Discovery in Databases. Research Track

Publisher :

Springer International Publishing

Publication place :

Cham

Publication date :

2021-09-10

HAL domain(s) :

Sciences de l'ingénieur [physics]
Mathématiques [math]
Informatique [cs]

English abstract : [en]

Generative Adversarial Networks (GANs) are performant generative methods yielding high-quality samples. However, under certain circumstances, the training of GANs can lead to mode collapse or mode dropping. To address this ...
Show more >Generative Adversarial Networks (GANs) are performant generative methods yielding high-quality samples. However, under certain circumstances, the training of GANs can lead to mode collapse or mode dropping. To address this problem, we use the last layer of the discriminator as a feature map to study the distribution of the real and the fake data. During training, we propose to match the real batch diversity to the fake batch diversity by using the Bures distance between covariance matrices in this feature space. The computation of the Bures distance can be conveniently done in either feature space or kernel space in terms of the covariance and kernel matrix respectively. We observe that diversity matching reduces mode collapse substantially and has a positive effect on sample quality. On the practical side, a very simple training procedure is proposed and assessed on several data sets.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Collections :