Geometry Score: A Method For Comparing Generative Adversarial Networks

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Valentin Khrulkov, Ivan Oseledets ;
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:2621-2629, 2018.

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.

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