{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T13:01:43Z","timestamp":1777035703700,"version":"3.51.4"},"reference-count":57,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T00:00:00Z","timestamp":1629072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["821966"],"award-info":[{"award-number":["821966"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Deep learning applications on computer vision involve the use of large-volume and representative data to obtain state-of-the-art results due to the massive number of parameters to optimise in deep models. However, data are limited with asymmetric distributions in industrial applications due to rare cases, legal restrictions, and high image-acquisition costs. Data augmentation based on deep learning generative adversarial networks, such as StyleGAN, has arisen as a way to create training data with symmetric distributions that may improve the generalisation capability of built models. StyleGAN generates highly realistic images in a variety of domains as a data augmentation strategy but requires a large amount of data to build image generators. Thus, transfer learning in conjunction with generative models are used to build models with small datasets. However, there are no reports on the impact of pre-trained generative models, using transfer learning. In this paper, we evaluate a StyleGAN generative model with transfer learning on different application domains\u2014training with paintings, portraits, Pok\u00e9mon, bedrooms, and cats\u2014to generate target images with different levels of content variability: bean seeds (low variability), faces of subjects between 5 and 19 years old (medium variability), and charcoal (high variability). We used the first version of StyleGAN due to the large number of publicly available pre-trained models. The Fr\u00e9chet Inception Distance was used for evaluating the quality of synthetic images. We found that StyleGAN with transfer learning produced good quality images, being an alternative for generating realistic synthetic images in the evaluated domains.<\/jats:p>","DOI":"10.3390\/sym13081497","type":"journal-article","created":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T21:28:04Z","timestamp":1629149284000},"page":"1497","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3432-3655","authenticated-orcid":false,"given":"Harold","family":"Achicanoy","sequence":"first","affiliation":[{"name":"Alliance of Bioversity International and CIAT, Km 17 Recta Cali-Palmira, Palmira 763537, Colombia"},{"name":"School of Computer and Systems Engineering, Universidad del Valle, Cali 760001, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7745-8111","authenticated-orcid":false,"given":"Deisy","family":"Chaves","sequence":"additional","affiliation":[{"name":"School of Computer and Systems Engineering, Universidad del Valle, Cali 760001, Colombia"},{"name":"Department of Electrical, Systems and Automation, Universidad de Le\u00f3n, 24007 Le\u00f3n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0169-1339","authenticated-orcid":false,"given":"Maria","family":"Trujillo","sequence":"additional","affiliation":[{"name":"School of Computer and Systems Engineering, Universidad del Valle, Cali 760001, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1007\/s11831-019-09344-w","article-title":"A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning","volume":"27","author":"Dargan","year":"2019","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., and Liu, C. (2018). A Survey on Deep Transfer Learning. arXiv.","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","article-title":"Learning from class-imbalanced data: Review of methods and applications","volume":"73","author":"Haixiang","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_4","unstructured":"Bowles, C., Chen, L., Guerrero, R., Bentley, P., Gunn, R., Hammers, A., Dickie, D.A., Hern\u00e1ndez, M.V., Wardlaw, J., and Rueckert, D. (2018). GAN augmentation: Augmenting training data using generative adversarial networks. arXiv."},{"key":"ref_5","unstructured":"Tanaka, F.H.K.d.S., and Aranha, C. (2019). Data Augmentation Using GANs. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_7","unstructured":"Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., and Weinberger, K.Q. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems 27, Curran Associates, Inc."},{"key":"ref_8","unstructured":"Antoniou, A., Storkey, A., and Edwards, H. (2017). Data augmentation generative adversarial networks. arXiv."},{"key":"ref_9","unstructured":"Garcia Torres, D. (2018). Generation of Synthetic Data with Generative Adversarial Networks. [Ph.D. Thesis, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology]."},{"key":"ref_10","unstructured":"Zeid Baker, M. (2018). Generation of Synthetic Images with Generative Adversarial Networks. [Master\u2019s Thesis, Department of Computer Science and Engineering, Blekinge Institute of Technology]."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ma, Y., Liu, K., Guan, Z., Xu, X., Qian, X., and Bao, H. (2018). Background Augmentation Generative Adversarial Networks (BAGANs): Effective Data Generation Based on GAN-Augmented 3D Synthesizing. Symmetry, 10.","DOI":"10.3390\/sym10120734"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Loey, M., Smarandache, F., and Khalifa, N.E.M. (2020). Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning. Symmetry, 12.","DOI":"10.3390\/sym12040651"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zulkifley, M.A., Abdani, S.R., and Zulkifley, N.H. (2020). COVID-19 Screening Using a Lightweight Convolutional Neural Network with Generative Adversarial Network Data Augmentation. Symmetry, 12.","DOI":"10.3390\/sym12091530"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"16884","DOI":"10.1038\/s41598-019-52737-x","article-title":"Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks","volume":"9","author":"Sandfort","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.neucom.2018.09.013","article-title":"GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification","volume":"321","author":"Diamant","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shin, H.C., Tenenholtz, N.A., Rogers, J.K., Schwarz, C.G., Senjem, M.L., Gunter, J.L., Andriole, K., and Michalski, M. (2018). Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks. arXiv.","DOI":"10.1007\/978-3-030-00536-8_1"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.zemedi.2020.05.001","article-title":"Latent Space Manipulation for High-Resolution Medical Image Synthesis via the StyleGAN","volume":"30","author":"Fetty","year":"2020","journal-title":"Z. Med. Phys."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.compind.2019.02.003","article-title":"Deep neural networks with transfer learning in millet crop images","volume":"108","author":"Coulibaly","year":"2019","journal-title":"Comput. Ind."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.imavis.2019.05.001","article-title":"Utilizing CNNs and transfer learning of pre-trained models for age range classification from unconstrained face images","volume":"88","author":"Qawaqneh","year":"2019","journal-title":"Image Vis. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.neucom.2018.10.100","article-title":"Fine-tuning Pre-trained Convolutional Neural Networks for Gastric Precancerous Disease Classification on Magnification Narrow-band Imaging Images","volume":"392","author":"Liu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1520\/SSMS20180033","article-title":"Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning","volume":"2","author":"Ferguson","year":"2018","journal-title":"Smart Sustain. Manuf. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105091","DOI":"10.1016\/j.compag.2019.105091","article-title":"Fine-tuning convolutional neural network with transfer learning for semantic segmentation of ground-level oilseed rape images in a field with high weed pressure","volume":"167","author":"Abdalla","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wu, C., Herranz, L., van de Weijer, J., Gonzalez-Garcia, A., and Raducanu, B. (2018). Transferring GANs: Generating images from limited data. arXiv.","DOI":"10.1007\/978-3-030-01231-1_14"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2019, January 16\u201320). A style-based generator architecture for generative adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_25","unstructured":"Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2018). Progressive Growing of GANs for Improved Quality, Stability, and Variation. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, X., and Belongie, S. (2017). Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization. arXiv.","DOI":"10.1109\/ICCV.2017.167"},{"key":"ref_27","unstructured":"Oeldorf, C. (2019, November 13). Conditional Implementation for NVIDIA\u2019s StyleGAN Architecture. Available online: https:\/\/github.com\/cedricoeldorf\/ConditionalStyleGAN."},{"key":"ref_28","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. Advances in Neural Information Processing Systems 30, Curran Associates, Inc."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2015). Rethinking the Inception Architecture for Computer Vision. arXiv.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Arsenovic, M., Karanovic, M., Sladojevic, S., Anderla, A., and Stefanovic, D. (2019). Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection. Symmetry, 11.","DOI":"10.3390\/sym11070939"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Arun Pandian, J., Geetharamani, G., and Annette, B. (2019, January 13\u201314). Data Augmentation on Plant Leaf Disease Image Dataset Using Image Manipulation and Deep Learning Techniques. Proceedings of the 2019 IEEE 9th International Conference on Advanced Computing (IACC), Tiruchirappalli, India.","DOI":"10.1109\/IACC48062.2019.8971580"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Noguchi, A., and Harada, T. (2019, January 27\u201328). Image generation from small datasets via batch statistics adaptation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00284"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Fregier, Y., and Gouray, J.B. (2021, January 21\u201323). Mind2Mind: Transfer learning for GANs. Proceedings of the International Conference on Geometric Science of Information, Paris, France.","DOI":"10.1007\/978-3-030-80209-7_91"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Luo, L., Hsu, W., and Wang, S. (2020, January 7\u20139). Data Augmentation Using Generative Adversarial Networks for Electrical Insulator Anomaly Detection. Proceedings of the 2020 2nd International Conference on Management Science and Industrial Engineering, Osaka, Japan.","DOI":"10.1145\/3396743.3396790"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hirte, A.U., Platscher, M., Joyce, T., Heit, J.J., Tranvinh, E., and Federau, C. (2020). Diffusion-Weighted Magnetic Resonance Brain Images Generation with Generative Adversarial Networks and Variational Autoencoders: A Comparison Study. arXiv.","DOI":"10.1016\/j.mri.2021.06.001"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"102037","DOI":"10.1016\/j.media.2021.102037","article-title":"Super-Resolution of Cardiac MR Cine Imaging using Conditional GANs and Unsupervised Transfer Learning","volume":"71","author":"Xia","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, Y., Gonzalez-Garcia, A., Berga, D., Herranz, L., Khan, F.S., and Weijer, J.V.D. (2020, January 14\u201319). Minegan: Effective knowledge transfer from gans to target domains with few images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00935"},{"key":"ref_39","unstructured":"Mo, S., Cho, M., and Shin, J. (2020). Freeze the discriminator: A simple baseline for fine-tuning gans. arXiv."},{"key":"ref_40","unstructured":"Zhao, M., Cong, Y., and Carin, L. (2020, January 13\u201318). On leveraging pretrained GANs for generation with limited data. Proceedings of the 37th International Conference on Machine Learning, Virtual Event."},{"key":"ref_41","unstructured":"Wang, Y., Gonzalez-Garcia, A., Wu, C., Herranz, L., Khan, F.S., Jui, S., and van de Weijer, J. (2021). MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains. arXiv."},{"key":"ref_42","first-page":"214","article-title":"Wasserstein Generative Adversarial Networks","volume":"Volume 70","author":"Precup","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T. (2020, January 14\u201319). Analyzing and Improving the Image Quality of StyleGAN. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref_44","unstructured":"Diaz, H. (2019). Bean seeds images calibration dataset. Alliance of Bioversity International and CIAT, Unpublished raw data."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Beebe, S. (2012). Breeding in the Tropics. Plant Breeding Reviews, John Wiley & Sons, Ltd.. Chapter 5.","DOI":"10.1002\/9781118358566.ch5"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Rothe, R., Timofte, R., and Gool, L.V. (2015, January 7\u201313). DEX: Deep EXpectation of apparent age from a single image. Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW), Santiago, Chile.","DOI":"10.1109\/ICCVW.2015.41"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R., Escalera, S., Bar\u00f3, X., Guyon, I., and Rothe, R. (June, January 30). Apparent and real age estimation in still images with deep residual regressors on APPA-REAL database. Proceedings of the FG 2017\u201412th IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC, USA.","DOI":"10.1109\/FG.2017.20"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., and Zafeiriou, S. (2017, January 21\u201326). AgeDB: The First Manually Collected, In-the-Wild Age Database. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.250"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ye, L., Li, B., Mohammed, N., Wang, Y., and Liang, J. (2018, January 29\u201331). Privacy-Preserving Age Estimation for Content Rating. Proceedings of the 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP), Vancouver, BC, Canada.","DOI":"10.1109\/MMSP.2018.8547144"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Anda, F., Lillis, D., Kanta, A., Becker, B.A., Bou-Harb, E., Le-Khac, N.A., and Scanlon, M. (2019, January 26\u201329). Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning. Proceedings of the 14th International Conference on Availability, Reliability and Security, Canterbury, UK.","DOI":"10.1145\/3339252.3341491"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chaves, D., Fidalgo, E., Alegre, E., J\u00e1\u00f1ez-Martino, F., and Biswas, R. (2020, January 27\u201319). Improving Age Estimation in Minors and Young Adults with Occluded Faces to Fight Against Child Sexual Exploitation. Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications\u2014Volume 5: VISAPP, Valletta, Malta. INSTICC.","DOI":"10.5220\/0008945907210729"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.powtec.2018.06.035","article-title":"Automatic characterisation of chars from the combustion of pulverised coals using machine vision","volume":"338","author":"Chaves","year":"2018","journal-title":"Powder Technol."},{"key":"ref_53","unstructured":"(2020, March 09). StyleGAN Trained on Paintings (512 \u00d7 512). Available online: https:\/\/colab.research.google.com\/drive\/1cFKK0CBnev2BF8z9BOHxePk7E-f7TtUi."},{"key":"ref_54","unstructured":"(2020, March 15). StyleGAN-Art. Available online: https:\/\/github.com\/ak9250\/stylegan-art."},{"key":"ref_55","unstructured":"(2020, March 15). StyleGAN-Pokemon. Available online: https:\/\/www.kaggle.com\/ahsenk\/stylegan-pokemon."},{"key":"ref_56","unstructured":"(2020, December 19). StyleGAN\u2014Official TensorFlow Implementation. Available online: https:\/\/github.com\/NVlabs\/stylegan."},{"key":"ref_57","unstructured":"Gwern (2020, March 09). Making Anime Faces With StyleGAN. Available online: https:\/\/www.gwern.net\/Faces."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/8\/1497\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:46:44Z","timestamp":1760165204000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/8\/1497"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,16]]},"references-count":57,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["sym13081497"],"URL":"https:\/\/doi.org\/10.3390\/sym13081497","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,16]]}}}