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...
Saved in:
Main Authors: | Nobari, A.H., Rashad, M.F., Ahmed, F. |
---|---|
Format: | Conference or Workshop Item |
Published: |
American Society of Mechanical Engineers (ASME)
2021
|
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/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Super resolution of car plate images using generative adversarial networks
by: Tan, K. L., et al.
Published: (2019) -
DR-LL Gan: diabetic retinopathy lesions synthesis using generative adversarial network
by: Abbood, Saif Hameed, et al.
Published: (2022) -
Medical image data upscaling with generative adversarial networks
by: Dobrovolny, Michal, et al.
Published: (2020) -
Hierarchical knee image synthesis framework for Generative adversarial network: Data from the osteoarthritis initiative
by: Gan, Hong-Seng, et al.
Published: (2022) -
Cycle generative adversarial network for unpaired sketch-to-character translation
by: Alsaati, Leena Zeini J.
Published: (2019)