Abstract
The purpose is to analyze the architectural art design of microscopic visual forms based on deep learning. First, GAN (Generative Adversarial Networks) of deep learning is applied to the field of architecture, and a multi-adversarial information sharing GAN is proposed through the improvement of GAN, and an architecture generation model of microscopic visual forms based on deep learning is constructed. The model is stimulated and its accuracy, distortion, and stability are analyzed. The results show that the accuracy of the model can reach more than 80% on different datasets. Compared with the other models in the related field, the model built in this study can show the features of the building images with the minimum distortion. Meanwhile, the curve hovers are around 0 in the process of model training, which is balanced. Therefore, the research can significantly improve the accuracy and the effect of feature extraction, and provide an experimental basis for the later architectural design of microscopic visual forms.
Similar content being viewed by others
References
Jia L, Ma Q, Du C et al (2020) Rapid urbanization in a mountainous landscape: patterns, drivers, and planning implications[J]. Landsc Ecol 35(11):2449–2469
Aggarwal HK, Mani M, Jacob M et al (2019) MoDL: model-based deep learning architecture for inverse problems[J]. IEEE Trans Med Imaging 38(2):394–405
Mutasa S, Chang P, Ruzalshapiro C et al (2018) MABAL: a novel deep-learning architecture for machine-assisted bone age labeling[J]. J Digit Imaging 31(4):513–519
Maggipinto M, Terzi M, Masiero C et al (2018) A computer vision-inspired deep learning architecture for virtual metrology modeling with 2-dimensional data[J]. IEEE Trans Semicond Manuf 31(3):376–384
Raj AP, Vajravelu SK (2019) DDLA: dual deep learning architecture for classification of plant species[J]. IET Image Proc 13(12):2176–2182
Ahmad F, Abbasi A, Li J et al (2020) A deep learning architecture for psychometric natural language processing[J]. ACM Trans Inf Syst 38(1):1–29
Antholzer S, Haltmeier M, Schwab J et al (2019) Deep learning for photoacoustic tomography from sparse data[J]. Inverse Probl Sci Eng 27(7):987–1005
Gupta S, Deep K (2020) A novel hybrid sine cosine algorithm for global optimization and its application to train multilayer perceptrons. Appl Intell 50(4):993–1026
Hamanah WM, Abido MA, Alhems LM (2020) Optimum sizing of hybrid pv, wind, battery and diesel system using lightning search algorithm[J]. Arab J Sci Eng 45(3):1871–1883
Jumani TA, Mustafa MW, Md Rasid M et al (2018) Optimal voltage and frequency control of an islanded microgrid using grasshopper optimization algorithm[J]. Energies 11(11):3191
Kaboli SHA, Selvaraj J, Rahim NA (2017) Rain-fall optimization algorithm: a population based algorithm for solving constrained optimization problems[J]. J Comput Sci 19:31–42
Alaa A, Alsewari AA, Alamri HS et al (2019) Comprehensive review of the development of the harmony search algorithm and its applications [J]. IEEE Access 7:14233–14245
Abualigah L, Diabat A (2021) Advances in sine cosine algorithm: a comprehensive survey[J]. Artif Intell Rev 54:1–42
Abualigah L (2020) Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications[J]. Neural Comput Appl 33:1–24
Abualigah L, Diabat A, Mirjalili S et al (2021) The arithmetic optimization algorithm[J]. Computer Methods Appl Mech Eng 376:113609
Lin J, Liu M, Hao J et al (2017) Many-objective harmony search for integrated order planning in steelmaking-continuous casting-hot rolling production of multi-plants[J]. Int J Prod Res 55(14):4003–4020
Abualigah L, Diabat A, Geem ZW (2020) A comprehensive survey of the harmony search algorithm in clustering applications[J]. Appl Sci 10(11):3827
Giuffrida MV, Doerner P, Tsaftaris SA et al (2018) Pheno-deep counter: a unified and versatile deep learning architecture for leaf counting[J]. Plant J 96(4):880–890
Wang P, Di J (2018) Deep learning-based object classification through multimode fiber via a CNN-architecture SpeckleNet[J]. Appl Opt 57(28):8258–8263
Trabelsi A, Chaabane M, Benhur A et al (2019) Comprehensive evaluation of deep learning architectures for prediction of DNA/RNA sequence binding specificities[J]. Bioinformatics 35(14):i269–i277
Chambon S, Thorey V, Arnal PJ et al (2019) DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal[J]. J Neurosci Methods 321:64–78
Luo F, Wang M, Liu Y et al (2019) DeepPhos: prediction of protein phosphorylation sites with deep learning[J]. Bioinformatics 35(16):2766–2773
Khokhlova OS, Nagler AO (2020) The Marfa Kurgan in the stavropol territory: an example of an ancient architectural structure[J]. Archaeol Ethnol Anthropol Eurasia 48(2):38–48
Trivizakis E, Ioannidis GS, Melissianos VD et al (2019) A novel deep learning architecture outperforming ‘off-the-shelf’ transfer learning and feature-based methods in the automated assessment of mammographic breast density[J]. Oncol Rep 42(5):2009–2015
Jindal A, Aujla GS, Kumar N et al (2018) SeDaTiVe: SDN-enabled deep learning architecture for network traffic control in vehicular cyber-physical systems[J]. IEEE Network 32(6):66–73
Fadlullah ZM, Mao B, Tang F et al (2019) Value iteration architecture based deep learning for intelligent routing exploiting heterogeneous computing platforms[J]. IEEE Trans Comput 68(6):939–950
Zhan Y, Zhang J, Li P et al (2019) Crowdtraining: architecture and incentive mechanism for deep learning training in the internet of things[J]. IEEE Network 33(5):89–95
Reynolds MJ, Gong R, Reyes SEDL et al (2020) Deep learning reveals the link between filament architecture and subunit conformation in bent actin[J]. Biophys J 118(3):124a–125a
Shin D, Lee J, Lee J et al (2018) DNPU: an energy-efficient deep-learning processor with heterogeneous multi-core architecture[J]. IEEE Micro 38(5):85–93
Chen H, Chen A, Xu L et al (2020) A deep learning CNN architecture applied in the smart near-infrared analysis of water pollution for agricultural irrigation resources[J]. Agric Water Manag 240:106303
Zhu J, Zeng H, Huang J et al (2020) Vehicle re-identification using quadruple directional deep learning Features[J]. IEEE Trans Intell Transp Syst 21(1):410–420
Sekhon A, Singh R, Qi Y (2018) DeepDiff: DEEP-learning for predicting DIFFerential gene expression from histone modifications[J]. Bioinformatics 34(17):i891–i900
Fan X, Wang F, Wang F et al (2019) When RFID meets deep learning: exploring cognitive intelligence for activity identification[J]. IEEE Wirel Commun 26(3):19–25
Peterson KT, Sagan V, Sloan JJ (2020) Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing[J]. GIScience Remote Sens 57(4):510–525
Wen X (2020) Using deep learning approach and IoT architecture to build the intelligent music recommendation system. Soft Comput. https://doi.org/10.1007/s00500-020-05364-y
Shen C-W, Min C, Wang C-C (2019) Analyzing the trend of O2O commerce by bilingual text mining on social media. Comput Hum Behav 101:474–483. https://doi.org/10.1016/j.chb.2018.09.031
Liu Y, Chen M (2018) From the aspect of STEM to discuss the effect of ecological art education on knowledge integration and problem-solving capability. Ekoloji 27(106):1705–1711
Acknowledgements
This work was supported by the Guangdong philosophy and Social Sciences Planning Project (Grant No: GD19YYS08)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Guo, Y. The microscopic visual forms in architectural art design following deep learning. J Supercomput 78, 559–577 (2022). https://doi.org/10.1007/s11227-021-03888-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-021-03888-0