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

31 Scopus citations


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

Bibliographical note

Cited By (since 2020): 6

M1 - Query date: 2021-03-11 11:12:31

M1 - 6 cites: https://scholar.google.com/scholar?cites=16391058196447072580&as_sdt=2005&sciodt=0,5&hl=en


Dive into the research topics of 'On leveraging pretrained GANs for generation with limited data'. Together they form a unique fingerprint.

Cite this