Zheng Y, Tuan LA, Novel A. Cognitively Inspired, Unified Graph-based Multi-Task Framework for Information Extraction. Cogn Comput. 2023;15:2004–13. https://doi.org/10.1007/s12559-023-10163-2 

Article  Google Scholar 

Gudivada VN, Pankanti S, Seetharaman G, Zhang Y. Cognitive computing systems: Their potential and the future. Comp. 2019;52(5):13–8.

Google Scholar 

Tabernik D, Šela S, Skvarč J, et al. Segmentation-based deep-learning approach for surface-defect detection. J Intell Manuf. 2020;31:759–76. https://doi.org/10.1007/s10845-019-01476-x.

Article  Google Scholar 

Tabernik D, Sela S, Skvarˇc J, Skoˇcaj D. Deep-learning-based com-puter vision system for surface-defect detection. In: In Computer Vision Sys-tems: 12th International Conference, ICVS 2019, Thessaloniki, Greece, September 23–25, 2019, Proceedings 12. Springer; 2019. p. 490–500.

Chapter  Google Scholar 

Ghaboura S, Ferdousi R, Laamarti F, Yang C, El Saddik A. Digital twin for railway: A comprehensive survey. IEEE Access. 2023;11:120237–57.

Article  Google Scholar 

Ferdousi R, Laamarti F, Yang C, El Saddik A. Railtwin: a digital twin framework for railway. In 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE). IEEE; 2022. p. 1767–72.

Google Scholar 

Yang C, Ferdousi R, El Saddik A, Li Y, Liu Z, Liao M. Lifetime learning-enabled modelling framework for digital twin. In: In 2022 IEEE 18th International Conference on Automa-tion Science and Engineering (CASE). 2022. p. 1761–6.

Google Scholar 

Cui S, Wang H, Zhang M, Zhang X. Defect classification on lim-ited labeled samples with multiscale. Appl Intell. 2021;51(6):3911–25.

Google Scholar 

Alqudah R, Al-Mousa AA, Hashyeh YA, Alzaibaq OZ. A systemic comparison between using augmented data and syn-thetic data as means of enhancing wafermap defect classification. Comput Ind. 2023;145:103809.

Article  Google Scholar 

Xiao Y, Huang Y, Li C, et al. Lightweight Multi-modal Representation Learning for RGB Salient Object Detection. Cogn Comput. 2023;15:1868–83. https://doi.org/10.1007/s12559-023-10148-1.

Article  Google Scholar 

Abufadda M, Mansour K. A survey of synthetic data generation for machine learning. In: In 2021 22nd international arab conference on information technology (ACIT). 2021. p. 1–7.

Google Scholar 

Jain S, Seth G, Paruthi A, et al. Synthetic data augmentation for surface defect detection and classification using deep learning. J Intell Manuf. 2022;33:1007–20. https://doi.org/10.1007/s10845-020-01710-x.

Article  Google Scholar 

Zhang G, Cui K, Hung TY, Lu S. Defect-gan: High-fidelity defect synthesis for automated defect inspection. In: In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2021. p. 2524–34.

Google Scholar 

Tevosyan A, Khondkaryan L, Khachatrian H, Tade-vosyan G, Apresyan L, Babayan N, Stopper H, Navoyan Z. Improving vae based molecular representations for compound property pre-diction. Journal of Cheminformatics. 2022;14(1):69.

Article  Google Scholar 

He X, Chang Z, Zhang L, Xu H, Chen H, Luo Z. A survey of de-fect detection applications based on generative adversarial networks. IEEE Access. 2022;10:113493–512.

Article  Google Scholar 

Lu Y, Shen M, Wang H, Wang X, van Rechem C, Wei W. Machine learning for synthetic data generation: a review. arXiv preprint arXiv:2302.04062, 2023.

Endres M, Mannarapotta Venugopal A, Tran TS. Syn-thetic data generation: a comparative study. In: In Proceedings of the 26th International Database Engineered Applications Symposium. 2022. p. 94–102.

Chapter  Google Scholar 

Pinheiro Cinelli L, Ara’ujo Marins M, Bar-ros da Silva EA, Lima Netto S. Variational autoencoder. In: In Varia-tional Methods for Machine Learning with Applications to Deep Networks. Springer; 2021. p. 111–49.

Chapter  Google Scholar 

Mak HWL, Han R, Yin HHF.  Application of variational autoencoder (vae) model and image processing approaches in game design. Sensors. 2023;23(7):3457.

Article  Google Scholar 

Kumar T, Mileo A, Brennan R, Bendechache M. Image data augmentation approaches: A comprehensive survey. arXiv preprint arXiv:2301.02830, 2023.

Wang R, Hoppe S, Monari E, Huber MF. De-fect transfer gan: Diverse defect synthesis for data augmentation. arXiv preprint arXiv:2302.08366, 2023.

Zhang G, Cui K, Hung TY, Lu S. Defect-gan: High-fidelity defect synthesis for automated defect inspection. arXiv preprint arXiv:2103.15158, 2021.

Jadon A, Kumar S. Leveraging generative ai models for synthetic data generation in healthcare: Balancing research and privacy. In:  In 2023 International Conference on Smart Applications, Communications and Networking (SmartNets). IEEE; 2023. p. 1–4.

Google Scholar 

Wang Z, Healy G, Smeaton AF, Ward TE. Use of neural signals to evaluate the quality of generative adversarial network performance in facial image generation. Cogn Comp. 2020;12:13–24.

Article  Google Scholar 

Alpaydin E. Introduction to machine learning. MIT press, 2020.

Shang H, Sun C, Liu J, Chen X, Yan R. Defect-aware transformer network for intelligent visual surface defect detection. Adv Eng Inform. 2023;55: 101882.

Article  Google Scholar 

Li J, Li D, Savarese S, Hoi S. Blip-2: Boot-strapping language-image pre-training with frozen image encoders and large language models. In: In International conference on machine learning. PMLR; 2023. p. 19730–42.

Google Scholar