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Geometry Score: A Method For Comparing Generative Adversarial Networks
Valentin Khrulkov · Ivan Oseledets

Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #85

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.

Author Information

Valentin Khrulkov (Skolkovo Institute Of Science And Technology)
Ivan Oseledets (Skoltech)

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