On leveraging pretrained GANs for generation with limited data

M Zhao, Y Cong, L Carin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

Recent work has shown generative adversarial networks (GANs) can generate highly realistic images, that are often indistinguishable (by humans) from real images. Most images so generated are not contained in the training dataset, suggesting potential for augmenting …
Original languageEnglish (US)
JournalInternational Conference on …
StatePublished - 2020
Externally publishedYes

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