Multiple Layout Design Generation via a GAN-Based Method with Conditional Convolution and Attention Article Swipe
Xing ZHU
,
Yuxuan LIU
,
Lingyu Liang
,
Tao Wang
,
Zuoyong Li
,
Qiaoming Deng
,
Yubo Liu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1587/transinf.2022edl8106
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1587/transinf.2022edl8106
Recently, many AI-aided layout design systems are developed to reduce tedious manual intervention based on deep learning. However, most methods focus on a specific generation task. This paper explores a challenging problem to obtain multiple layout design generation (LDG), which generates floor plan or urban plan from a boundary input under a unified framework. One of the main challenges of multiple LDG is to obtain reasonable topological structures of layout generation with irregular boundaries and layout elements for different types of design. This paper formulates the multiple LDG task as an image-to-image translation problem, and proposes a conditional generative adversarial network (GAN), called LDGAN, with adaptive modules. The framework of LDGAN is based on a generator-discriminator architecture, where the generator is integrated with conditional convolution constrained by the boundary input and the attention module with channel and spatial features. Qualitative and quantitative experiments were conducted on the SCUT-AutoALP and RPLAN datasets, and the comparison with the state-of-the-art methods illustrate the effectiveness and superiority of the proposed LDGAN.
Related Topics
Concepts
Computer science
Discriminator
Generator (circuit theory)
Convolution (computer science)
Task (project management)
Boundary (topology)
Computer engineering
Focus (optics)
Plan (archaeology)
Image (mathematics)
Artificial intelligence
Theoretical computer science
Systems engineering
Mathematics
Artificial neural network
Power (physics)
Detector
Mathematical analysis
History
Engineering
Archaeology
Telecommunications
Optics
Physics
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1587/transinf.2022edl8106
- OA Status
- diamond
- Cited By
- 1
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386315308
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386315308Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1587/transinf.2022edl8106Digital Object Identifier
- Title
-
Multiple Layout Design Generation via a GAN-Based Method with Conditional Convolution and AttentionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-08-31Full publication date if available
- Authors
-
Xing ZHU, Yuxuan LIU, Lingyu Liang, Tao Wang, Zuoyong Li, Qiaoming Deng, Yubo LiuList of authors in order
- Landing page
-
https://doi.org/10.1587/transinf.2022edl8106Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1587/transinf.2022edl8106Direct OA link when available
- Concepts
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Computer science, Discriminator, Generator (circuit theory), Convolution (computer science), Task (project management), Boundary (topology), Computer engineering, Focus (optics), Plan (archaeology), Image (mathematics), Artificial intelligence, Theoretical computer science, Systems engineering, Mathematics, Artificial neural network, Power (physics), Detector, Mathematical analysis, History, Engineering, Archaeology, Telecommunications, Optics, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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18Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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