SC20 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

How Good Is Your Scientific Data Generative Model?


Workshop:AI4S: Workshop on Artificial Intelligence and Machine Learning for Scientific Applications

Authors: Yuxin Yang (Kent State University), Ben Gremillion (University of Texas), Xitong Zhang (Michigan State University), and Youzuo Lin and Brendt Wohlberg (Los Alamos National Laboratory)


Abstract: Nowadays, leveraging data augmentation methods on helping resolving scientific problems becomes prevailing. And many scientific problems benefit from data augmentation methods build with deep generative models. Yet due to the complexity of the scientific data, commonly used evaluation methods of generative models appear not so suitable for generated scientific data. In this paper, we explore how do we effectively evaluate data augmentation methods for scientific data generative models? To answer this question, we use one example of real world scientific problem to show how we evaluate the quality of the generated data from two domain specific deep generative models. We observe that most existing state-of-art evaluation metrics are incompetent. They either show completely contradicting results or provide inaccurate insight from real data.





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