Cumincad : CUMINCAD References : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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	Mokhtar, S., Sojka, A. and Davila, C.C. (2020) Conditional Generative Adversarial Networks for Pedestrian Wind Flow Approximation , Proceedings of SimAUD 2020

	Mokhtar, S., Sojka, A., & Davila, C. C. (n.d.) Conditional Generative Adversarial Networks for Pedestrian Wind Flow Approximation , 11th annual Symposium on Simulation for Architecture and Urban Design (SimAUD)

	Mokhtar, S., Sojka, A., Davila, & C. C. (2020) Conditional Generative Adversarial Networks for Pedestrian Wind Flow Approximation , Proceedings of the 11th Annual Symposium on Simulation for Architecture and Urban Design (Vol. 58, pp. 1-8). San Diego, USA; Society for Computer Simulation International

	Mostafa A and Al-Kaseem B (2021) Fill in the blanks: Deep convolutional generative adversarial networks to investigate the virtual design space of historical islamic patterns , 9th ASCAAD Conf Proc 2021: 614–621.

	Mueller, L.M., Andriotis, C., Turrin, M. (2024) Using Generative Adversarial Networks to Create 3D Building Geometries: 3DBuildingGAN , eCAADe 2024

	Nauata N, Chang K, Cheng C, et al (2020) House-gan: Relational generative adversarial networks for graph-constrained house layout generation , Eur Conf Computer Vis 2020: 162–177.

	Nauata, N, Chang, KH, Cheng, CY, Mori, G and Furukawa, Y (2020) House-GAN: Relational Generative Adversarial Networks for Graph-Constrained House Layout Generation , arXiv:2003.06988 [cs]

	Nauata, N, Chang, KH, Cheng, CY, Mori, G and Furukawa, Y (2020) House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation , Computer Vision - ECCV 2020 Lecture Notes in Computer Science, p. 162-177

	Nauata, N, K. Chang, C. Chin-Yi, G. Mori, and Y. Furukawa (2020) House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation , European Conference on Computer Vision. arXiv preprint. arXiv:2003.06988

	Nauata, N., Chang, K. H., Cheng, C. Y., Mori, G., & Furukawa, Y. (2020) House-gan: Relational generative adversarial networks for graph-constrained house layout generation , Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part I 16 (pp. 162-177). Springer International Publishing

	Nauata, N., Chang, K. H., Cheng, C. Y., Mori, G., & Furukawa, Y. (2020) House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation , arXiv preprint arXiv:2003.06988

	Nauata, N., Chang, K. H., Cheng, C. Y., Mori, G., & Furukawa, Y. (2020) House-gan: Relational generative adversarial networks for graph-constrained house layout generation , Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part I 16 (pp. 162-177). Springer International Publishing

	Nauata, N., Chang, K. H., Cheng, C. Y., Mori, G., & Furukawa, Y. (2020) House-gan: Relational generative adversarial networks for graph-constrained house layout generation , Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part I 16 (pp. 162-177). Springer International Publishing. https://doi.org/10.1007/978-3-030-58452-8_10

	Nauata, N., Chang, K. H., Cheng, C. Y., Mori, G., Furukawa, Y. (2020) House-gan: Relational generative adversarial networks for graph-constrained house layout generation , European Conference on Computer Vision (ECCV). pp. 162-177. Available at: https://doi.org/10.1007/978-3-030-58452-8

	Nauata, N., Chang, K.-H., Cheng, C.-Y., Mori, G. & Furukawa, Y. (2020) House-GAN: Relational Generative Adversarial Networks for Graph-Constrained House Layout Generation , A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Eds.), Proceedings of the European Conference on Computer Vision (ECCV) (pp. 162–177). Springer International Publishing. https://doi.org/10.1007/978-3-030-58452-8_10

	Nayak, Mishak. (2018) Deep Convolutional Generative Adversarial Networks (DCGANs) , Data Driven Investor. https://medium.com/ datadriveninvestor/deep-convolutional-generative-adversarial- networks-dcgans-3176238b5a3d

	Nelson Nauata, Sepidehsadat Hosseini, Kai-Hung Chang, Hang Chu, Chin-Yi Cheng, and Yasutaka Furukawa (2021) House-GAN++: Generative Adversarial Layout Refinement Networks , arXiv preprintarXiv: 2103.02574

	Nie, Z., Lin, T., Jiang, H. & Kara, L. B. (2021) Topologygan: Topology optimisation using generative adversarial networks based on physical fields over the initial domain , Journal of Mechanical Design, 143(3), 031715

	Paganini, M, de Oliveira, L and Nachman, B (2018) CaloGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks , Physical Review D, 97(1), p. 014021

	Park, K., Ergan, S. and Feng, C. (2024) Quality assessment of residential layout designs generated by relational Generative Adversarial Networks (GANs) , Automation in Construction, 158, p. 105243

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