2
ActFloor-GAN: Activity-Guided Adversarial Networks for Human-Centric Floorplan Design
3
Generating Residential Layout Based on AI in the View of Wind Environment
4
ATISS: Autoregressive Transformers for Indoor Scene Synthesis
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House-GAN++: Generative Adversarial Layout Refinement Networks
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A Review of AI for Urban Planning: Towards Building Sustainable Smart Cities
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Optimizing Hospital Room Layout to Reduce the Risk of Patient Falls
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House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation
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A Spatial Adaptive Algorithm Framework for Building Pattern Recognition Using Graph Convolutional Networks
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Data-driven interior plan generation for residential buildings
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New Quantitative Approach for the Morphological Similarity Analysis of Urban Fabrics Based on a Convolutional Autoencoder
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A graph convolutional neural network for classification of building patterns using spatial vector data
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LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators
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Artificial intelligence in architecture: Generating conceptual design via deep learning
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Deep convolutional priors for indoor scene synthesis
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Recognition of building group patterns in topographic maps based on graph partitioning and random forest
19
Generative Adversarial Networks: An Overview
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A machine learning-based method for the large-scale evaluation of the qualities of the urban environment
21
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
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Deep Reinforcement Learning: An Overview
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Image-to-Image Translation with Conditional Adversarial Networks
24
Machine Learning: The New AI
25
Semi-Supervised Classification with Graph Convolutional Networks
27
Tutorial on Variational Autoencoders
28
Concept of Interactive Machine Learning in Urban Design Problems
29
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
30
Conditional Generative Adversarial Nets
31
Data matching of building polygons at multiple map scales improved by contextual information and relaxation
32
Game level layout from design specification
33
Generating and exploring good building layouts
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Deep learning comes of age
35
Make it home: automatic optimization of furniture arrangement
36
Computer-generated residential building layouts
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Diversity analysis on imbalanced data sets by using ensemble models
38
Wallplan: synthesizing floorplans by learning to generate wall graphs
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Generative design method of forced layout in residential area based on cgan
40
Computer-aided approach to public buildings floor plan generation. Magnetizing Floor Plan Generator
41
The Development of Optimization Methods in Generative Urban Design: A Review
42
Automated parametric building volume generation: a case study for urban blocks
43
pytorch-fid: FID Score for PyTorch
44
GENERATIVE ADVERSARIAL NETS
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StreetGAN: towards road network synthesis with generative adversarial networks
47
Outdoor space quality: A field study in an urban residential community in central China
48
Coordinate systems and map projections
49
Rethinking Automated Layout Design: Developing a Creative Evolutionary Design Method for the Layout Problems in Architecture and Urban Design
50
Learning Deep Architectures for AI
51
Reinforcement Learning: An Introduction
52
Shapefile technical description
53
Did you discuss any potential negative societal impacts of your work?
54
with respect to the random seed after running experiments multiple times)? [N/A] Since our experiments are based on a DCGAN-similar [27] model and only to demonstrate the capabilities of our dataset
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(a) Did you state the full set of assumptions of all theoretical results
56
Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
57
Did you include the code, data, and instructions needed to reproduce the main experimental results
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ReCo: A Dataset for Residential Community Layout Planning
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c) Did you include any new assets either in the supplemental material or as a URL?
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Version 0.2.1. (c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times
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Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation
63
b) Did you mention the license of the assets? [Yes]
65
Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes]
66
Did you discuss whether and how consent was obtained from people whose data you’re using/curating? [Yes]
67
code, data, models) or curating/releasing new assets... (a) If your work uses existing assets
68
Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable?
69
If you used crowdsourcing or conducted research with human subjects
