CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis

CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis

<jats:title>Abstract</jats:title> <jats:p>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...

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Bibliographic Details
Main Authors: Heyrani Nobari, Amin, Rashad, Muhammad Fathy, Ahmed, Faez
Other Authors: Massachusetts Institute of Technology. Department of Mechanical Engineering
Format: Article
Language:English
Published: ASME International 2023
Online Access:https://hdl.handle.net/1721.1/150665

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