{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T19:28:40Z","timestamp":1773948520223,"version":"3.50.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T00:00:00Z","timestamp":1705449600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T00:00:00Z","timestamp":1705449600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["H2020- 956573"],"award-info":[{"award-number":["H2020- 956573"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018960","name":"National Technical University of Athens","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100018960","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Visual defect recognition and its manufacturing applications have been an upcoming topic in recent AI research. Defect datasets are often severely imbalanced and can be additionally burdened with separating classes of high visual similarity. Although various methods of data augmentation have been proposed to mitigate the class imbalance, they often fail to cope with tinier minority classes or have fidelity issues with smaller defects while, at the same time, needing significant computational resources to train. Also, augmentation based on vector-based oversampling struggles to produce high-fidelity inputs and is hard to apply on custom CNN architectures, which often perform better for this type of problem. Our work presents an image-level oversampling method based on an instance-based image generator that can be applied to any CNN directly during the training process without increasing the order of training time required. It is based on identifying a small number of the most uncertain base samples close to the estimated class boundaries and using them as seeds for augmentation. The resulting images are of high visual quality preserving small class differences, and they also improve the classifier boundary leading to higher recall scores than other state-of-the-art approaches.<\/jats:p>","DOI":"10.1007\/s10994-023-06498-4","type":"journal-article","created":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T17:37:29Z","timestamp":1705513049000},"page":"4013-4035","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["On-the-fly image-level oversampling for imbalanced datasets of manufacturing defects"],"prefix":"10.1007","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3491-5682","authenticated-orcid":false,"given":"Spyros","family":"Theodoropoulos","sequence":"first","affiliation":[]},{"given":"Patrik","family":"Zajec","sequence":"additional","affiliation":[]},{"given":"Jo\u017ee M.","family":"Ro\u017eanec","sequence":"additional","affiliation":[]},{"given":"Dimosthenis","family":"Kyriazis","sequence":"additional","affiliation":[]},{"given":"Panayiotis","family":"Tsanakas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,17]]},"reference":[{"key":"6498_CR1","doi-asserted-by":"publisher","DOI":"10.3390\/sym13081497","author":"H Achicanoy","year":"2021","unstructured":"Achicanoy, H., Chaves, D., & Trujillo, M. (2021). Stylegans and transfer learning for generating synthetic images in industrial applications. Symmetry. https:\/\/doi.org\/10.3390\/sym13081497","journal-title":"Symmetry"},{"key":"6498_CR2","doi-asserted-by":"crossref","unstructured":"Ak\u00e7ay, S., Atapour-Abarghouei, A. & Breckon, T. (2019). Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly detection. In 2019 International Joint Conference on Neural Networks (IJCNN), 1\u20138","DOI":"10.1109\/IJCNN.2019.8851808"},{"key":"6498_CR3","doi-asserted-by":"publisher","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D. & Steger, C. (2019). Mvtec ad - a comprehensive real-world dataset for unsupervised anomaly detection. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9584\u20139592. https:\/\/doi.org\/10.1109\/CVPR.2019.00982","DOI":"10.1109\/CVPR.2019.00982"},{"key":"6498_CR4","unstructured":"Berthelot, D., Schumm, T. & Metz, L. (2017). Began: Boundary equilibrium generative adversarial networks. arXiv:1703.10717"},{"key":"6498_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.scienta.2021.110684","volume":"293","author":"JJ Bird","year":"2022","unstructured":"Bird, J. J., Barnes, C. M., Manso, L. J., Ekart, A., & Faria, D. R. (2022). Fruit quality and defect image classification with conditional GAN data augmentation. Scientia Horticulturae, 293, 110684. https:\/\/doi.org\/10.1016\/j.scienta.2021.110684","journal-title":"Scientia Horticulturae"},{"key":"6498_CR6","unstructured":"Brock, A., Donahue, J. & Simonyan, K. (2019). Large scale gan training for high fidelity natural image synthesis. ArXiv abs\/1809.11096"},{"issue":"1","key":"6498_CR7","first-page":"321","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). Smote: Synthetic minority over-sampling technique. J. Artif. Int. Res., 16(1), 321\u2013357.","journal-title":"J. Artif. Int. Res."},{"key":"6498_CR8","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I. & Abbeel, P. (2016). Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In NIPS"},{"key":"6498_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3136503","author":"D Dablain","year":"2022","unstructured":"Dablain, D., Krawczyk, B., & Chawla, N. V. (2022). Deepsmote: Fusing deep learning and smote for imbalanced data. IEEE Transactions on Neural Networks and Learning Systems. https:\/\/doi.org\/10.1109\/TNNLS.2021.3136503","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"6498_CR10","doi-asserted-by":"crossref","unstructured":"Dalal, N. & Triggs, B. (2005). Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), vol. 1, pp. 886\u2013893. Ieee","DOI":"10.1109\/CVPR.2005.177"},{"key":"6498_CR11","unstructured":"Elsayed, G.F., Krishnan, D., Mobahi, H., Regan, K. & Bengio, S. (2018). Large margin deep networks for classification. arXiv:1803.05598pdf"},{"key":"6498_CR12","doi-asserted-by":"publisher","first-page":"2734","DOI":"10.1109\/ACCESS.2020.3047838","volume":"9","author":"Y Fathy","year":"2021","unstructured":"Fathy, Y., Jaber, M., & Brintrup, A. (2021). Learning with imbalanced data in smart manufacturing: A comparative analysis. IEEE Access, 9, 2734\u20132757. https:\/\/doi.org\/10.1109\/ACCESS.2020.3047838","journal-title":"IEEE Access"},{"key":"6498_CR13","doi-asserted-by":"publisher","DOI":"10.3390\/math9192359","author":"X Feng","year":"2021","unstructured":"Feng, X., Gao, X., & Luo, L. (2021). A resnet50-based method for classifying surface defects in hot-rolled strip steel. Mathematics. https:\/\/doi.org\/10.3390\/math9192359","journal-title":"Mathematics"},{"key":"6498_CR14","doi-asserted-by":"publisher","unstructured":"Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J. & Greenspan, H. (2018). Synthetic data augmentation using gan for improved liver lesion classification. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 289\u2013293. https:\/\/doi.org\/10.1109\/ISBI.2018.8363576","DOI":"10.1109\/ISBI.2018.8363576"},{"key":"6498_CR15","unstructured":"Goodfellow, I., Bengio, Y. & Courville, A. (2016). Deep Learning. MIT Press. http:\/\/www.deeplearningbook.org"},{"key":"6498_CR16","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. & Bengio, Y. (2014). Generative adversarial nets. In Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., Weinberger, K.Q. (Eds.) Advances in Neural Information Processing Systems, vol. 27. Curran Associates, Inc."},{"key":"6498_CR17","unstructured":"Guo, C., Pleiss, G., Sun, Y. & Weinberger, K.Q. (2017). On calibration of modern neural networks. In Proceedings of the 34th International Conference on Machine Learning, vol. 70. ICML\u201917, pp. 1321\u20131330. JMLR.org."},{"key":"6498_CR18","doi-asserted-by":"publisher","unstructured":"Han, C., Murao, K., Noguchi, T., Kawata, Y., Uchiyama, F., Rundo, L., Nakayama, H. & Satoh, S. (2019). Learning more with less: Conditional pggan-based data augmentation for brain metastases detection using highly-rough annotation on mr images. CIKM \u201919, pp. 119\u2013127. Association for Computing Machinery, New York. https:\/\/doi.org\/10.1145\/3357384.3357890.","DOI":"10.1145\/3357384.3357890"},{"key":"6498_CR19","doi-asserted-by":"crossref","unstructured":"Han, H., Wang, W. & Mao, B. (2005). Borderline-smote: A new over-sampling method in imbalanced data sets learning. In ICIC","DOI":"10.1007\/11538059_91"},{"key":"6498_CR20","unstructured":"Hinton, G., Vinyals, O. & Dean, J. (2015). Distilling the knowledge in a neural network. In NIPS Deep Learning and Representation Learning Workshop. arXiv:1503.02531"},{"key":"6498_CR21","doi-asserted-by":"publisher","unstructured":"Huang, L., Lin, K. C. J. & Tseng, Y. C. (2019). Resolving intra-class imbalance for gan-based image augmentation. In 2019 IEEE International Conference on Multimedia and Expo (ICME), pp. 970\u2013975. https:\/\/doi.org\/10.1109\/ICME.2019.00171","DOI":"10.1109\/ICME.2019.00171"},{"key":"6498_CR22","doi-asserted-by":"crossref","unstructured":"Jain, S., Seth, G., Paruthi, A., Soni, U. & Kumar, G. (2020). Synthetic data augmentation for surface defect detection and classification using deep learning. Journal of Intelligent Manufacturing, 1\u201314","DOI":"10.1007\/s10845-020-01710-x"},{"key":"6498_CR23","unstructured":"Jiang, Y., Chang, S. & Wang, Z. (2021). Transgan: Two transformers can make one strong gan. arXiv preprint arXiv:2102.07074"},{"key":"6498_CR24","doi-asserted-by":"publisher","unstructured":"Kadar, M. & Onita, D. (2019). A deep cnn for image analytics in automated manufacturing process control. In 2019 11th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1\u20135 . https:\/\/doi.org\/10.1109\/ECAI46879.2019.9042159","DOI":"10.1109\/ECAI46879.2019.9042159"},{"key":"6498_CR25","unstructured":"Karras, T., Aila, T., Laine, S. & Lehtinen, J. (2018). Progressive growing of GANs for improved quality, stability, and variation. arXiv:1710.10196"},{"key":"6498_CR26","unstructured":"Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J. & Aila, T. (2020). Training generative adversarial networks with limited data. In Proc. NeurIPS"},{"key":"6498_CR27","unstructured":"Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J. & Aila, T. (2020). Training generative adversarial networks with limited data. In Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS\u201920. Curran Associates Inc., Red Hook."},{"issue":"12","key":"6498_CR28","doi-asserted-by":"publisher","first-page":"4217","DOI":"10.1109\/TPAMI.2020.2970919","volume":"43","author":"T Karras","year":"2021","unstructured":"Karras, T., Laine, S., & Aila, T. (2021). A style-based generator architecture for generative adversarial networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 43(12), 4217\u20134228. https:\/\/doi.org\/10.1109\/TPAMI.2020.2970919","journal-title":"IEEE Transactions on Pattern Analysis & Machine Intelligence"},{"issue":"6","key":"6498_CR29","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84\u201390.","journal-title":"Communications of the ACM"},{"key":"6498_CR30","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.neucom.2019.09.107","volume":"408","author":"X Le","year":"2020","unstructured":"Le, X., Mei, J., Zhang, H., Zhou, B., & Xi, J. (2020). A learning-based approach for surface defect detection using small image datasets. Neurocomputing, 408, 112\u2013120. https:\/\/doi.org\/10.1016\/j.neucom.2019.09.107","journal-title":"Neurocomputing"},{"key":"6498_CR31","doi-asserted-by":"publisher","unstructured":"Liu, L., Cao, D., Wu, Y. & Wei, T. (2019). Defective samples simulation through adversarial training for automatic surface inspection. Neurocomput. 360(C), 230\u2013245. https:\/\/doi.org\/10.1016\/j.neucom.2019.05.080","DOI":"10.1016\/j.neucom.2019.05.080"},{"key":"6498_CR32","doi-asserted-by":"publisher","first-page":"108162","DOI":"10.1109\/ACCESS.2021.3101247","volume":"9","author":"D Li","year":"2021","unstructured":"Li, D., Xie, W., Wang, B., Zhong, W., & Wang, H. (2021). Data augmentation and layered deformable mask r-CNN-based detection of wood defects. IEEE Access, 9, 108162\u2013108174. https:\/\/doi.org\/10.1109\/ACCESS.2021.3101247","journal-title":"IEEE Access"},{"key":"6498_CR33","doi-asserted-by":"publisher","unstructured":"Luan, F., Paris, S., Shechtman, E. & Bala, K. (2018). Deep painterly harmonization. Computer Graphics Forum 37. https:\/\/doi.org\/10.1111\/cgf.13478","DOI":"10.1111\/cgf.13478"},{"issue":"1","key":"6498_CR34","doi-asserted-by":"publisher","DOI":"10.1088\/1755-1315\/354\/1\/012106","volume":"354","author":"Z Luo","year":"2019","unstructured":"Luo, Z., Cheng, S. Y., & Zheng, Q. Y. (2019). GAN-based augmentation for improving CNN performance of classification of defective photovoltaic module cells in electroluminescence images. IOP Conference Series: Earth and Environmental Science, 354(1), 012106. https:\/\/doi.org\/10.1088\/1755-1315\/354\/1\/012106","journal-title":"IOP Conference Series: Earth and Environmental Science"},{"key":"6498_CR35","doi-asserted-by":"publisher","unstructured":"Meister, S., Mueller, N., Stoeve, J. & Groves, R. (2021). Synthetic image data augmentation for fibre layup inspection processes: Techniques to enhance the data set. Journal of Intelligent Manufacturing. https:\/\/doi.org\/10.1007\/s10845-021-01738-7","DOI":"10.1007\/s10845-021-01738-7"},{"key":"6498_CR36","unstructured":"Moon, J., Kim, J.-h., Shin, Y. & Hwang, S. (2020). Confidence-aware learning for deep neural networks. In ICML"},{"key":"6498_CR37","unstructured":"M\u00fcller, R., Kornblith, S. & Hinton, G. E. (2019). When does label smoothing help? In NeurIPS"},{"key":"6498_CR38","unstructured":"Naeini, M. P., Cooper, G. F. & Hauskrecht, M. (2015). Obtaining well calibrated probabilities using bayesian binning. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. AAAI\u201915, pp. 2901\u20132907. AAAI Press."},{"key":"6498_CR39","doi-asserted-by":"publisher","unstructured":"Niculescu-Mizil, A. & Caruana, R. (2005). Predicting good probabilities with supervised learning. In Proceedings of the 22nd International Conference on Machine Learning. ICML \u201905, pp 625\u2013632. Association for Computing Machinery, New York. https:\/\/doi.org\/10.1145\/1102351.1102430.","DOI":"10.1145\/1102351.1102430"},{"issue":"3","key":"6498_CR40","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1109\/TASE.2020.2967415","volume":"17","author":"S Niu","year":"2020","unstructured":"Niu, S., Li, B., Wang, X., & Lin, H. (2020). Defect image sample generation with GAN for improving defect recognition. IEEE Transactions on Automation Science and Engineering, 17(3), 1611\u20131622. https:\/\/doi.org\/10.1109\/TASE.2020.2967415","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"key":"6498_CR41","doi-asserted-by":"crossref","unstructured":"Noguchi, A. & Harada, T. (2019). Image generation from small datasets via batch statistics adaptation. In 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), 2750\u20132758","DOI":"10.1109\/ICCV.2019.00284"},{"key":"6498_CR42","unstructured":"Odena, A., Olah, C. & Shlens, J. (2017). Conditional image synthesis with auxiliary classifier gans. In Proceedings of the 34th International Conference on Machine Learning, vol 70. ICML\u201917, pp. 2642\u20132651. JMLR.org."},{"key":"6498_CR43","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1007\/978-3-319-70353-4_52","volume-title":"Advanced Concepts for Intelligent Vision Systems","author":"P Pawara","year":"2017","unstructured":"Pawara, P., Okafor, E., Schomaker, L., & Wiering, M. (2017). Data augmentation for plant classification. In J. Blanc-Talon, R. Penne, W. Philips, D. Popescu, & P. Scheunders (Eds.), Advanced Concepts for Intelligent Vision Systems (pp. 615\u2013626). Springer."},{"key":"6498_CR44","doi-asserted-by":"publisher","unstructured":"Peres, R.S., Azevedo, M., Araujo, S.O., Guedes, M., Miranda, F. & Barata, J. (2021). Generative adversarial networks for data augmentation in structural adhesive inspection. Applied Sciences11(7). https:\/\/doi.org\/10.3390\/app11073086","DOI":"10.3390\/app11073086"},{"key":"6498_CR45","unstructured":"Platt, J. (2000). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classif. 10"},{"key":"6498_CR46","doi-asserted-by":"publisher","unstructured":"Saiz, F.A., Alfaro, G., Barandiaran, I. & Grana, M. (2021). Generative adversarial networks to improve the robustness of visual defect segmentation by semantic networks in manufacturing components. Applied Sciences11(14). https:\/\/doi.org\/10.3390\/app11146368","DOI":"10.3390\/app11146368"},{"key":"6498_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00414-0","volume":"8","author":"V Sampath","year":"2021","unstructured":"Sampath, V., Maurtua, I., Aguilar Mart\u00edn, J. J., & Gutierrez, A. (2021). A survey on generative adversarial networks for imbalance problems in computer vision tasks. Journal of Big Data, 8, 1\u20132. https:\/\/doi.org\/10.1186\/s40537-021-00414-0","journal-title":"Journal of Big Data"},{"key":"6498_CR48","unstructured":"Satoshi\u00a0Tsutsui, D. C. & Yanwei, F. (2019). Meta-reinforced synthetic data for one-shot fine-grained visual recognition. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"6498_CR49","unstructured":"See, J.E. (2012). Visual inspection : a review of the literature. Sandia Report SAND2012-8590, Sandia National Laboratories, Albuquerque, New Mexico"},{"key":"6498_CR50","unstructured":"Sohn, K., Lee, H. & Yan, X. (2015). Learning structured output representation using deep conditional generative models. In Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (Eds.) Advances in Neural Information Processing Systems, vol. 28. Curran Associates, Inc."},{"issue":"2","key":"6498_CR51","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","volume":"36","author":"M Stone","year":"1974","unstructured":"Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111\u2013133. https:\/\/doi.org\/10.1111\/j.2517-6161.1974.tb00994.x","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"key":"6498_CR52","doi-asserted-by":"publisher","unstructured":"Tang, T. W., Kuo, W. H., Lan, J. H., Ding, C. F., Hsu, H. & Young, H. T. (2020). Anomaly detection neural network with dual auto-encoders GAN and its industrial inspection applications. Sensors20(12). https:\/\/doi.org\/10.3390\/s20123336","DOI":"10.3390\/s20123336"},{"key":"6498_CR53","doi-asserted-by":"crossref","unstructured":"Thulasidasan, S., Chennupati, G., Bilmes, J., Bhattacharya, T. & Michalak, S. (2019). On mixup training: Improved calibration and predictive uncertainty for deep neural networks. arXiv:abs\/1905.11001","DOI":"10.2172\/1525811"},{"key":"6498_CR54","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.jare.2021.03.015","volume":"35","author":"A-A Tulbure","year":"2022","unstructured":"Tulbure, A.-A., Tulbure, A.-A., & Dulf, E.-H. (2022). A review on modern defect detection models using DCNNs\u2014deep convolutional neural networks. Journal of Advanced Research, 35, 33\u201348. https:\/\/doi.org\/10.1016\/j.jare.2021.03.015","journal-title":"Journal of Advanced Research"},{"key":"6498_CR55","doi-asserted-by":"crossref","unstructured":"Viola, P. & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, p. IEEE","DOI":"10.1109\/CVPR.2001.990517"},{"key":"6498_CR56","doi-asserted-by":"publisher","unstructured":"Wang, C. & Xiao, Z. (2021). Lychee surface defect detection based on deep convolutional neural networks with GAN-based data augmentation. Agronomy 11(8). https:\/\/doi.org\/10.3390\/agronomy11081500","DOI":"10.3390\/agronomy11081500"},{"issue":"2","key":"6498_CR57","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1952\/2\/022010","volume":"1952","author":"Y Wang","year":"2021","unstructured":"Wang, Y., Luo, S., & Wu, H. (2021). Defect detection of solar cell based on data augmentation. Journal of Physics: Conference Series, 1952(2), 022010. https:\/\/doi.org\/10.1088\/1742-6596\/1952\/2\/022010","journal-title":"Journal of Physics: Conference Series"},{"key":"6498_CR58","doi-asserted-by":"publisher","first-page":"1210","DOI":"10.1002\/sdtp.14096","volume":"51","author":"W Xiong","year":"2020","unstructured":"Xiong, W., Lee, J., Qu, S., & Jang, W. (2020). Data augmentation for applying deep learning to display manufacturing defect detection. SID Symposium Digest of Technical Papers, 51, 1210\u20131213. https:\/\/doi.org\/10.1002\/sdtp.14096","journal-title":"SID Symposium Digest of Technical Papers"},{"key":"6498_CR59","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.jmsy.2020.03.009","volume":"55","author":"JP Yun","year":"2020","unstructured":"Yun, J. P., Shin, W. C., Koo, G., Kim, M. S., Lee, C., & Lee, S. J. (2020). Automated defect inspection system for metal surfaces based on deep learning and data augmentation. Journal of Manufacturing Systems, 55, 317\u2013324. https:\/\/doi.org\/10.1016\/j.jmsy.2020.03.009","journal-title":"Journal of Manufacturing Systems"},{"key":"6498_CR60","doi-asserted-by":"publisher","unstructured":"Zadrozny, B. & Elkan, C. (2002). Transforming classifier scores into accurate multiclass probability estimates. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD \u201902, pp. 694\u2013699. Association for Computing Machinery, New York. https:\/\/doi.org\/10.1145\/775047.775151.","DOI":"10.1145\/775047.775151"},{"key":"6498_CR61","doi-asserted-by":"publisher","unstructured":"Zhang, H., Chen, Z., Zhang, C., Xi, J. & Le, X. (2019). Weld defect detection based on deep learning method. In 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), pp. 1574\u20131579. https:\/\/doi.org\/10.1109\/COASE.2019.8842998","DOI":"10.1109\/COASE.2019.8842998"},{"key":"6498_CR62","doi-asserted-by":"crossref","unstructured":"Zhang, G., Cui, K., Hung, T. Y. & Lu, S. (2021). Defect-gan: High-fidelity defect synthesis for automated defect inspection. In 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2523\u20132533.","DOI":"10.1109\/WACV48630.2021.00257"},{"key":"6498_CR63","doi-asserted-by":"publisher","unstructured":"Zhang, G., Cui, K., Hung, T.-Y. & Lu, S. (2021). Defect-gan: High-fidelity defect synthesis for automated defect inspection. In 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 2523\u20132533. https:\/\/doi.org\/10.1109\/WACV48630.2021.00257","DOI":"10.1109\/WACV48630.2021.00257"},{"key":"6498_CR64","doi-asserted-by":"publisher","DOI":"10.3390\/info12100397","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Wa, S., Sun, P., & Wang, Y. (2021). Pear defect detection method based on RESNET and DCGAN. Information. https:\/\/doi.org\/10.3390\/info12100397","journal-title":"Information"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-023-06498-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-023-06498-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-023-06498-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T18:48:48Z","timestamp":1717181328000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-023-06498-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,17]]},"references-count":64,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["6498"],"URL":"https:\/\/doi.org\/10.1007\/s10994-023-06498-4","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,17]]},"assertion":[{"value":"8 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"Not Applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not Applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All authors who participated in this study give the publisher the permission to publish this work.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}