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A Practical Guide to Statistical Distances for Evaluating Generative Models in Science
March 20, 2024, 4:41 a.m. | Sebastian Bischoff, Alana Darcher, Michael Deistler, Richard Gao, Franziska Gerken, Manuel Gloeckler, Lisa Haxel, Jaivardhan Kapoor, Janne K Lappalain
cs.LG updates on arXiv.org arxiv.org
Abstract: Generative models are invaluable in many fields of science because of their ability to capture high-dimensional and complicated distributions, such as photo-realistic images, protein structures, and connectomes. How do we evaluate the samples these models generate? This work aims to provide an accessible entry point to understanding popular notions of statistical distances, requiring only foundational knowledge in mathematics and statistics. We focus on four commonly used notions of statistical distances representing different methodologies: Using low-dimensional …
abstract arxiv cs.lg fields generate generative generative models guide images photo practical protein protein structures samples science statistical stat.ml type work
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