Creativegan: Editing generative adversarial networks for creative design synthesis
Modern machine learning techniques, such as deep neural networks, are transforming many disciplines ranging from image recognition to language understanding, by uncovering patterns in big data and making accurate predictions. They have also shown promising results for synthesizing new designs, which...
| Main Authors: | Nobari, A.H., Rashad, M.F., Ahmed, F. |
|---|---|
| Format: | Conference or Workshop Item |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.29514 / |
| Published: |
American Society of Mechanical Engineers (ASME)
2021
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108620325&doi=10.1115%2fDETC2021-68103&partnerID=40&md5=5cd2774428d5683d1e9d76a878d182f9 http://eprints.utp.edu.my/29514/ |
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