Graph Transformer GANs With Graph Masked Modeling for Architectural Layout Generation

We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for challenging graph-constrained architectural layout generation tasks. The proposed graph-Transformer-based generator includes a novel graph Transformer enco...

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Publié dans:	IEEE transactions on pattern analysis and machine intelligence. - 1979. - 46(2024), 6 vom: 02. Juni, Seite 4298-4313
Auteur principal:	Tang, Hao (Auteur)
Autres auteurs:	Shao, Ling, Sebe, Nicu, Van Gool, Luc
Format:	Article en ligne
Langue:	English
Publié:	2024
Accès à la collection:	IEEE transactions on pattern analysis and machine intelligence
Sujets:	Journal Article