Page 67 - Read Online
P. 67

Hu et al. J. Mater. Inf. 2025, 5, 44  https://dx.doi.org/10.20517/jmi.2025.21   Page 17 of 17

               34.      Bachman, P.; Hjelm, R. D.; Buchwalter, W. Learning representations by maximizing mutual information across views. arXiv 2019,
                   arXiv:1906.00910. https://doi.org/10.48550/arXiv.1906.00910. (accessed 10 Jul 2025)
               35.      Pöppelbaum, J.; Chadha, G. S.; Schwung, A. Contrastive learning based self-supervised time-series analysis. Appl. Soft. Comput. 2022,
                   117, 108397.  DOI
               36.      He, K.; Fan, H.; Wu, Y.; Xie, S.; Girshick, R. Momentum contrast for unsupervised visual representation learning. In 2020 IEEE/CVF
                   Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA. Jun 13-19, 2020. IEEE; 2020. pp. 9726-35.  DOI
               37.      Chen, T.; Kornblith, S.; Norouzi, M.; Hinton, G. A simple framework for contrastive learning of visual representations. In Proceedings
                   of the 37th International Conference on Machine Learning. PMLR; 2020. pp. 1597-607. https://proceedings.mlr.press/v119/chen20j.
                   html. (accessed 10 Jul 2025)
               38.      Chen, X.; He, K. Exploring simple siamese representation learning. In 2021 IEEE/CVF Conference on Computer Vision and Pattern
                   Recognition (CVPR), Nashiville, USA. Jun 20-25, 2021. IEEE; 2021. pp. 15745-53.  DOI
               39.      Song, K.; Yan, Y. A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl. Surf.
                   Sci. 2013, 285, 858-64.  DOI
               40.      Gui, J.; Chen, T.; Zhang, J.; Cao, Q.; Sun, Z.; Luo, H. A survey on self-supervised learning: algorithms, applications, and future trends.
                   IEEE. Trans. Pattern. Anal. Mach. Intell. 2024, 46, 9052-71.  DOI
               41.      Zhao, Z.; Alzubaidi, L.; Zhang, J.; Duan, Y.; Gu, Y. A comparison review of transfer learning and self-supervised learning: definitions,
                   applications, advantages and limitations. Expert. Syst. Appl. 2024, 242, 122807.  DOI
               42.      Huang, J.; Rathod, V.; Sun, C.; et al. Speed/accuracy trade-offs for modern convolutional object detectors. arXiv 2016, arXiv:1611.
                   10012. https://doi.org/10.48550/arXiv.1611.10012. (accessed 10 Jul 2025)
               43.      Lin, T. Y.; Dollár, P.; Girshick, R.; He, K.; Hariharan, B.; Belongie, S. Feature pyramid networks for object detection. In 2017 IEEE
                   Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA. Jul 21-26, 2017. IEEE; 2017. pp. 936-44.  DOI
               44.      Lin, T. Y.; Maire, M.; Belongie, S.; et al. Microsoft COCO: common objects in context. arXiv 2014, arXiv:1405.0312. https://doi.org/
                   10.48550/arXiv.1405.0312. (accessed 10 Jul 2025)
               45.      Li, Z.; Wei, X.; Jiang, X. SSDD-Net: a lightweight and efficient deep learning model for steel surface defect detection. In Pattern
                   Recognition and Computer Vision: 6th Chinese Conference, PRCV 2023, Xiamen, China. Oct 13-15, 2023. Springer-Verlag; 2023. pp.
                   237-48.  DOI
               46.      Li, M.; Wei, L.; Zheng, B. Steel surface defect detection based on improved YOLOv7. In 2024 4th International Conference on
                   Computer, Control and Robotics (ICCCR), Shanghai, China. Apr 19-21, 2024. IEEE; 2024. pp. 51-5.  DOI
   62   63   64   65   66   67   68   69   70   71   72