PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems

28 Oct 2019Alexander PotapovIan ColbertKen Kreutz-DelgadoAlexander CloningerSrinjoy Das

Stochastic-sampling-based Generative Neural Networks, such as Restricted Boltzmann Machines and Generative Adversarial Networks, are now used for applications such as denoising, image occlusion removal, pattern completion, and motion synthesis. In scenarios which involve performing such inference tasks with these models, it is critical to determine metrics that allow for model selection and/or maintenance of requisite generative performance under pre-specified implementation constraints... (read more)

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