{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T03:31:06Z","timestamp":1782963066009,"version":"3.54.5"},"reference-count":76,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T00:00:00Z","timestamp":1655942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Federation of Industrial Research Associations (AiF)","award":["grant number 19811 N"],"award-info":[{"award-number":["grant number 19811 N"]}]},{"name":"German Federation of Industrial Research Associations (AiF)","award":["FKZ: 01IS21049C"],"award-info":[{"award-number":["FKZ: 01IS21049C"]}]},{"name":"German Federal Ministry of Education and Research (BMBF)","award":["grant number 19811 N"],"award-info":[{"award-number":["grant number 19811 N"]}]},{"name":"German Federal Ministry of Education and Research (BMBF)","award":["FKZ: 01IS21049C"],"award-info":[{"award-number":["FKZ: 01IS21049C"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fabric anomaly detection (AD) tries to detect anomalies (i.e., defects) in fabrics, and fabric AD approaches are continuously improved with respect to their AD performance. However, developed solutions are known to generalize poorly to previously unseen fabrics, posing a crucial limitation to their applicability. Moreover, current research focuses on adapting converged models to previously unseen fabrics in a post hoc manner, rather than training models that generalize better in the first place. In our work, we explore this potential for the first time. Specifically, we propose that previously unseen fabrics can be regarded as shifts in the underlying data distribution. We therefore argue that factors which reportedly improve a model\u2019s resistance to distribution shifts should also improve the performance of supervised fabric AD methods on unseen fabrics. Hence, we assess the potential benefits of: (I) vicinal risk minimization (VRM) techniques adapted to the fabric AD use-case, (II) different loss functions, (III) ImageNet pre-training, (IV) dataset diversity, and (V) model architecture as well as model complexity. The subsequently performed large-scale analysis reveals that (I) only the VRM technique, AugMix, consistently improves performance on unseen fabrics; (II) hypersphere classifier outperforms other loss functions when combined with AugMix and (III) ImageNet pre-training, which is already beneficial on its own; (IV) increasing dataset diversity improves performance on unseen fabrics; and (V) architectures with better ImageNet performance also perform better on unseen fabrics, yet the same does not hold for more complex models. Notably, the results show that not all factors and techniques which reportedly improve a model\u2019s resistance to distribution shifts in natural images also improve the generalization of supervised fabric AD methods to unseen fabrics, demonstrating the necessity of our work. Additionally, we also assess whether the performance gains of models which generalize better propagate to post hoc adaptation methods and show this to be the case. Since no suitable fabric dataset was publicly available at the time of this work, we acquired our own fabric dataset, called OLP, as the basis for the above experiments. OLP consists of 38 complex, patterned fabrics, more than 6400 images in total, and is made publicly available.<\/jats:p>","DOI":"10.3390\/s22134750","type":"journal-article","created":{"date-parts":[[2022,6,23]],"date-time":"2022-06-23T22:43:00Z","timestamp":1656024180000},"page":"4750","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Increasing the Generalization of Supervised Fabric Anomaly Detection Methods to Unseen Fabrics"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4556-5094","authenticated-orcid":false,"given":"Oliver","family":"Rippel","sequence":"first","affiliation":[{"name":"Institute of Imaging & Computer Vision, RWTH Aachen University, 52062 Aachen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8322-0003","authenticated-orcid":false,"given":"Corinna","family":"Zwinge","sequence":"additional","affiliation":[{"name":"Institute of Imaging & Computer Vision, RWTH Aachen University, 52062 Aachen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1672-2185","authenticated-orcid":false,"given":"Dorit","family":"Merhof","sequence":"additional","affiliation":[{"name":"Institute of Imaging & Computer Vision, RWTH Aachen University, 52062 Aachen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1080\/00405009208631217","article-title":"FDAS: A Knowledge-based Framework for Analysis of Defects in Woven Textile Structures","volume":"83","author":"Srinivasan","year":"1992","journal-title":"J. Text. Inst."},{"key":"ref_2","first-page":"828","article-title":"Allgemeine Betrachtungen zur Gewebeinspektion an der Webmaschine","volume":"81","year":"2000","journal-title":"Melliand Textilberichte"},{"key":"ref_3","unstructured":"Karayiannis, Y.A., Stojanovic, R., Mitropoulos, P., Koulamas, C., Stouraitis, T., Koubias, S., and Papadopoulos, G. (1999, January 5\u20138). Defect detection and classification on web textile fabric using multiresolution decomposition and neural networks. Proceedings of the ICECS\u201999 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357), Paphos, Cyprus."},{"key":"ref_4","unstructured":"See, J.E. (2012). Visual Inspection: A Review of the Literature, Sandia National Laboratories. Sandia Report SAND2012-8590."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1006\/cviu.1995.1017","article-title":"A survey of automated visual inspection","volume":"61","author":"Newman","year":"1995","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection: A survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1109\/JPROC.2021.3052449","article-title":"A Unifying Review of Deep and Shallow Anomaly Detection","volume":"109","author":"Ruff","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mei, S., Wang, Y., and Wen, G. (2018). Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model. Sensors, 18.","DOI":"10.3390\/s18041064"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1177\/0040517519862880","article-title":"Unsupervised fabric defect detection based on a deep convolutional generative adversarial network","volume":"90","author":"Hu","year":"2020","journal-title":"Text. Res. J."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tian, H., and Li, F. (2019, January 27\u201331). Autoencoder-Based Fabric Defect Detection with Cross- Patch Similarity. Proceedings of the 2019 16th International Conference on Machine Vision Applications (MVA), Tokyo, Japan.","DOI":"10.23919\/MVA.2019.8758051"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Han, Y.J., and Yu, H.J. (2020). Fabric Defect Detection System Using Stacked Convolutional Denoising Auto-Encoders Trained with Synthetic Defect Data. Appl. Sci., 10.","DOI":"10.3390\/app10072511"},{"key":"ref_12","unstructured":"Wong, W.K. Woven Fabric Defect Detection Based on Convolutional Neural Network for Binary Classification. Proceedings of the Artificial Intelligence on Fashion and Textiles."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., and Lopez-Paz, D. (2017). mixup: Beyond Empirical Risk Minimization. arXiv.","DOI":"10.1007\/978-1-4899-7687-1_79"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3539","DOI":"10.1177\/0040517518813656","article-title":"A new method using the convolutional neural network with compressive sensing for fabric defect classification based on small sample sizes","volume":"89","author":"Wei","year":"2019","journal-title":"Text. Res. J."},{"key":"ref_15","first-page":"1","article-title":"Fabric Defect Segmentation Method Based on Deep Learning","volume":"70","author":"Huang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"108885","DOI":"10.1016\/j.measurement.2020.108885","article-title":"EDDs: A series of Efficient Defect Detectors for fabric quality inspection","volume":"172","author":"Zhou","year":"2021","journal-title":"Measurement"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/j.ins.2020.05.050","article-title":"D4Net: De-deformation defect detection network for non-rigid products with large patterns","volume":"547","author":"Xu","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rippel, O., M\u00fcller, M., and Merhof, D. (2020, January 8\u201311). GAN-based Defect Synthesis for Anomaly Detection in Fabrics. Proceedings of the 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vienna, Austria.","DOI":"10.1109\/ETFA46521.2020.9212099"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1109\/TAI.2021.3057027","article-title":"ZJU-Leaper: A Benchmark Dataset for Fabric Defect Detection and a Comparative Study","volume":"1","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3388","DOI":"10.1109\/TIP.2019.2959741","article-title":"Multistage GAN for Fabric Defect Detection","volume":"29","author":"Liu","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rippel, O., M\u00fcller, M., M\u00fcnkel, A., Gries, T., and Merhof, D. (2021, January 4\u20136). Estimating the Probability Density Function of New Fabrics for Fabric Anomaly Detection. Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods\u2014ICPRAM, INSTICC, Vienna, Austria.","DOI":"10.5220\/0010163604630470"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., and Guo, M. (2021, January 11\u201317). The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00823"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kornblith, S., Shlens, J., and Le, Q.V. (2019, January 15\u201320). Do better imagenet models transfer better?. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00277"},{"key":"ref_24","unstructured":"Lv, K., Jiang, S., and Li, J. (2017, January 6\u201311). Learning Gradient Descent: Better Generalization and Longer Horizons. Proceedings of the 34th International Conference on Machine Learning, PMLR, Sydney, Australia."},{"key":"ref_25","unstructured":"Hendrycks, D., Lee, K., and Mazeika, M. (2019, January 9\u201315). Using pre-training can improve model robustness and uncertainty. Proceedings of the International Conference on Machine Learning. PMLR, Long Beach, CA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wu, Y., Zhang, X., and Fang, F. (2020). Automatic Fabric Defect Detection Using Cascaded Mixed Feature Pyramid with Guided Localization. Sensors, 20.","DOI":"10.3390\/s20030871"},{"key":"ref_28","first-page":"1","article-title":"DefectNet: Toward Fast and Effective Defect Detection","volume":"70","author":"Li","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Peng, P., Wang, Y., Hao, C., Zhu, Z., Liu, T., and Zhou, W. (2020). Automatic Fabric Defect Detection Method Using PRAN-Net. Appl. Sci., 10.","DOI":"10.3390\/app10238434"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Weninger, L., Kopaczka, M., and Merhof, D. (2018, January 14\u201317). Defect Detection in Plain Weave Fabrics by Yarn Tracking and Fully Convolutional Networks. Proceedings of the IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Houston, TX, USA.","DOI":"10.1109\/I2MTC.2018.8409546"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.neucom.2019.09.107","article-title":"A learning-based approach for surface defect detection using small image datasets","volume":"408","author":"Le","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, G., Cui, K., Hung, T.Y., and Lu, S. (2021, January 3\u20138). Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00257"},{"key":"ref_33","unstructured":"Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y. (May, January 30). Spectral Normalization for Generative Adversarial Networks. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_34","unstructured":"Schulz-Mirbach, H. (1996). Ein Referenzdatensatz zur Evaluierung von Sichtpr\u00fcfungsverfahren f\u00fcr Textiloberfl\u00e4chen, Technical Report; Technische Universit\u00e4t Hamburg."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"363","DOI":"10.2478\/aut-2019-0035","article-title":"A public fabric database for defect detection methods and results","volume":"19","author":"Miralles","year":"2019","journal-title":"Autex Res. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1109\/TASE.2008.917140","article-title":"Regularity Analysis for Patterned Texture Inspection","volume":"6","author":"Ngan","year":"2009","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_37","unstructured":"Guangdong Industrial Smart Manufacturing Innovation Competition (2022, February 10). Technical Report. Available online: https:\/\/tianchi.aliyun.com\/competition\/entrance\/231748\/."},{"key":"ref_38","unstructured":"Schneider, D. (2015). On-Loom Fabric Defect Detection: State-of-the-Art and Beyond. [Ph.D. Thesis, RWTH Aachen University]."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Nayak, R., and Padhye, R. (2018). 4-Automation in fabric inspection. Automation in Garment Manufacturing, Woodhead Publishing.","DOI":"10.1016\/B978-0-08-101211-6.00001-X"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"11960","DOI":"10.1016\/j.ijleo.2016.09.110","article-title":"Fabric defect detection systems and methods\u2014A systematic literature review","volume":"24","author":"Hanbay","year":"2016","journal-title":"Opt.-Int. J. Light Electron Opt."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bandara, P., Bandara, T., Ranatunga, T., Vimarshana, V., Sooriyaarachchi, S., and Silva, C.D. (2018, January 26\u201329). Automated Fabric Defect Detection. Proceedings of the 18th International Conference on Advances in ICT for Emerging Regions (ICTer), Colombo, Sri Lanka.","DOI":"10.1109\/ICTER.2018.8615491"},{"key":"ref_42","unstructured":"ASTM International (2020). Standard No. Standard ASTM D3990-12(2020)."},{"key":"ref_43","unstructured":"Ahmed, A. (2022, February 10). A Catalogue of Visual Textile Defects. Available online: https:\/\/aasimahmed.files.wordpress.com\/2008\/05\/catalogue-of-visual-textile-defects-aasim-ahmed.pdf."},{"key":"ref_44","unstructured":"Recht, B., Roelofs, R., Schmidt, L., and Shankar, V. (2019, January 10\u201315). Do imagenet classifiers generalize to imagenet?. Proceedings of the International Conference on Machine Learning, PMLR, Long Beach, CA, USA."},{"key":"ref_45","unstructured":"Hendrycks, D., Mu, N., Cubuk, E.D., Zoph, B., Gilmer, J., and Lakshminarayanan, B. (2019). AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. arXiv."},{"key":"ref_46","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., and Yoo, Y. (November, January 27). Cutmix: Regularization strategy to train strong classifiers with localizable features. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_47","unstructured":"DeVries, T., and Taylor, G.W. (2017). Improved Regularization of Convolutional Neural Networks with Cutout. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q.V. (2019, January 15\u201320). Autoaugment: Learning augmentation strategies from data. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00020"},{"key":"ref_49","first-page":"702","article-title":"RandAugment: Practical Automated Data Augmentation with a Reduced Search Space","volume":"33","author":"Cubuk","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Mensink, T., Uijlings, J., Kuznetsova, A., Gygli, M., and Ferrari, V. (2021). Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types. arXiv.","DOI":"10.1109\/TPAMI.2021.3129870"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J.H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_53","unstructured":"Ruff, L., Vandermeulen, R.A., Franks, B.J., M\u00fcller, K.R., and Kloft, M. (2020). Rethinking Assumptions in Deep Anomaly Detection. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_56","unstructured":"Tan, M., and Le, Q.V. (2019, January 9\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, ICML 2019, Long Beach, CA, USA."},{"key":"ref_57","unstructured":"Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019 Alch\u00e9-Buc, F., Fox, E., and Garnett, R. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems 32, Curran Associates, Inc."},{"key":"ref_58","unstructured":"Bishop, C. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1214\/aos\/1176344136","article-title":"Estimating the dimension of a model","volume":"6","author":"Schwarz","year":"1978","journal-title":"Ann. Stat."},{"key":"ref_60","unstructured":"Meila, M., and Zhang, T. (2021, January 18\u201324). Transfer-Based Semantic Anomaly Detection. Proceedings of the 38th International Conference on Machine Learning, Virtual Event."},{"key":"ref_61","unstructured":"Jiang, Y., Neyshabur, B., Mobahi, H., Krishnan, D., and Bengio, S. (2020, January 26\u201330). Fantastic Generalization Measures and Where to Find Them. Proceedings of the International Conference on Learning Representations, Ababa, Ethiopia."},{"key":"ref_62","unstructured":"Natekar, P., and Sharma, M. (2020). Representation Based Complexity Measures for Predicting Generalization in Deep Learning. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Davis, J., and Goadrich, M. (2006, January 25\u201329). The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143874"},{"key":"ref_64","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA."},{"key":"ref_65","first-page":"369","article-title":"Super-convergence: Very fast training of neural networks using large learning rates","volume":"Volume 11006","author":"Pham","year":"2019","journal-title":"Proceedings of the Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., and Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. arXiv.","DOI":"10.21105\/joss.00861"},{"key":"ref_67","unstructured":"Sohn, K., Yoon, J., Li, C.L., Lee, C.Y., and Pfister, T. (2021). Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types. arXiv."},{"key":"ref_68","first-page":"211","article-title":"A classification framework for anomaly detection","volume":"6","author":"Steinwart","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref_69","unstructured":"Chapelle, O., Schlkopf, B., and Zien, A. (2010). Semi-Supervised Learning, The MIT Press. [1st ed.]."},{"key":"ref_70","unstructured":"Mustafa, B., Loh, A., Freyberg, J., MacWilliams, P., Wilson, M., McKinney, S.M., Sieniek, M., Winkens, J., Liu, Y., and Bui, P. (2021). Supervised transfer learning at scale for medical imaging. arXiv."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Stutz, D., Hein, M., and Schiele, B. (2019, January 15\u201320). Disentangling Adversarial Robustness and Generalization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00714"},{"key":"ref_72","unstructured":"Hendrycks, D., and Dietterich, T. (2019, January 6\u20139). Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_73","unstructured":"Xie, C., and Yuille, A. (2020, January 26\u201330). Intriguing Properties of Adversarial Training at Scale. Proceedings of the International Conference on Learning Representations, Addis Ababa, Ethiopia."},{"key":"ref_74","first-page":"1","article-title":"A Pixel-Level Segmentation Convolutional Neural Network Based on Deep Feature Fusion for Surface Defect Detection","volume":"70","author":"Cao","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_75","first-page":"1","article-title":"SDDNet: A Fast and Accurate Network for Surface Defect Detection","volume":"70","author":"Cui","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"6637","DOI":"10.1109\/TIP.2021.3096067","article-title":"Reference-Based Defect Detection Network","volume":"30","author":"Zeng","year":"2021","journal-title":"IEEE Trans. Image Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4750\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:38:42Z","timestamp":1760139522000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4750"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,23]]},"references-count":76,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22134750"],"URL":"https:\/\/doi.org\/10.3390\/s22134750","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,23]]}}}