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Geometry Score: A Method For Comparing Generative Adversarial Networks

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jan 2018Embargo end date: 01 Jan 2018Publisher:arXiv

Authors: Khrulkov, Valentin; Oseledets, Ivan;

doi: 10.48550/arxiv.1802.02664

arXiv: http://arxiv.org/abs/1802.02664

Geometry Score: A Method For Comparing Generative Adversarial Networks

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Abstract

One of the biggest challenges in the research of generative adversarial networks (GANs) is assessing the quality of generated samples and detecting various levels of mode collapse. In this work, we construct a novel measure of performance of a GAN by comparing geometrical properties of the underlying data manifold and the generated one, which provides both qualitative and quantitative means for evaluation. Our algorithm can be applied to datasets of an arbitrary nature and is not limited to visual data. We test the obtained metric on various real-life models and datasets and demonstrate that our method provides new insights into properties of GANs.

ICML 2018

Keywords

Computational Geometry (cs.CG), FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Computational Geometry, Machine Learning (stat.ML), Machine Learning (cs.LG)

4 Research products, page 1 of 1

geometry-score software on GitHub
IsRelatedTo
CaloGAN software on GitHub
IsRelatedTo
DCGAN-tensorflow software on GitHub
IsRelatedTo
improved_wgan_training software on GitHub
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