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2023
Presentation
Title

Statistical Property Testing for Generative Models

Title Supplement
Published as a Tiny Paper at ICLR 2023
Abstract
Generative models that produce images, text, or other types of data are recently be equipped with more powerful capabilities. Nevertheless, in some use cases of the generated data (e.g., using it for model training), one must ensure that the synthetic data points satisfy some properties that make them suitable for the intended use. Towards this goal, we present a simple framework to statistically check if the data produced by a generative model satisfy some property with a given confidence level. We apply our methodology to standard image and text-to-image generative models.
Author(s)
Seferis, Emmanouil
Fraunhofer-Institut für Kognitive Systeme IKS  
Burton, Simon  
Fraunhofer-Institut für Kognitive Systeme IKS  
Cheng, Chih-Hong  
Fraunhofer-Institut für Kognitive Systeme IKS  
Project(s)
IKS-Ausbauprojekt  
Funder
Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie  
Conference
International Conference on Learning Representations 2023  
DOI
10.24406/publica-1473
File(s)
Seferis_StatisticalPropertyTestingForGenerativeModels_2305_ICLR.pdf (598.41 KB)
Rights
Under Copyright
Language
English
Fraunhofer-Institut für Kognitive Systeme IKS  
Fraunhofer Group
Fraunhofer-Verbund IUK-Technologie  
Keyword(s)
  • generative model

  • statistical testing

  • deep neural networks

  • DNN

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