{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T11:00:39Z","timestamp":1793012439938,"version":"4.5.0"},"reference-count":129,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T00:00:00Z","timestamp":1686355200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T00:00:00Z","timestamp":1686355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100012957","name":"Friedrich-Schiller-Universit\u00e4t Jena","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100012957","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The role of quality control based on images is important in industrial production. Nevertheless, this problem has not been addressed in computer vision for a long time. In recent years, this has changed: driven by publicly available datasets, a variety of methods have been proposed for detecting anomalies and defects in workpieces. In this survey, we present more than 40 methods that promise the best results for this task. In a comprehensive benchmark, we show that more datasets and metrics are needed to move the field forward. Further, we highlight strengths and weaknesses, discuss research gaps and future research areas.<\/jats:p>","DOI":"10.1007\/s11263-023-01822-w","type":"journal-article","created":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T14:01:56Z","timestamp":1686405716000},"page":"2553-2581","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A Survey of Methods for Automated Quality Control Based on Images"],"prefix":"10.1007","volume":"131","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0900-3801","authenticated-orcid":false,"given":"Jan","family":"Diers","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Pigorsch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,10]]},"reference":[{"key":"1822_CR1","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","volume":"6","author":"A Adadi","year":"2018","unstructured":"Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138\u201352160.","journal-title":"IEEE Access"},{"key":"1822_CR2","unstructured":"Bae, J., Lee, J.-H., & Kim, S. (2022). Image anomaly detection and localization with position and neighborhood information. arXiv:2211.12634."},{"key":"1822_CR3","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798\u20131828.","DOI":"10.1109\/TPAMI.2013.50"},{"key":"1822_CR4","unstructured":"Bergman, L., & Hoshen, Y. (2020). Classification-based anomaly detection for general data. arXiv:2005.02359."},{"key":"1822_CR5","unstructured":"Bergman, L., Cohen, N., & Hoshen, Y. (2020). Deep nearest neighbor anomaly detection. arXiv:2002.10445."},{"key":"1822_CR6","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2020). Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 4182\u20134191). ISBN 2575-7075.","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"1822_CR7","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Jin, X., Sattlegger, D., & Steger, C. (2021b). The MVTEC 3D-Ad dataset for unsupervised 3D anomaly detection and localization. arXiv:2112.09045.","DOI":"10.5220\/0010865000003124"},{"key":"1822_CR8","doi-asserted-by":"crossref","unstructured":"Bergmann, P., L\u00f6we, S., Fauser, M., Sattlegger, D., & Steger, C. (2018). Improving unsupervised defect segmentation by applying structural similarity to autoencoders. arXiv:1807.02011.","DOI":"10.5220\/0007364503720380"},{"issue":"4","key":"1822_CR9","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1007\/s11263-020-01400-4","volume":"129","author":"P Bergmann","year":"2021","unstructured":"Bergmann, P., Batzner, K., Fauser, M., Sattlegger, D., & Steger, C. (2021). The MVTEC anomaly detection dataset: A comprehensive real-world dataset for unsupervised anomaly detection. International Journal of Computer Vision, 129(4), 1038\u20131059.","journal-title":"International Journal of Computer Vision"},{"key":"1822_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3444690","volume":"54","author":"A Bl\u2019azquez-Garc\u2019ia","year":"2020","unstructured":"Bl\u2019azquez-Garc\u2019ia, A., Conde, A., Mori, U., & Lozano, J. A. (2020). A review on outlier\/anomaly detection in time series data. ACM Computing Surveys (CSUR), 54, 1\u201333.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"1822_CR11","unstructured":"Bommasani, R., Hudson, D. A., & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv:2108.07258."},{"key":"1822_CR12","doi-asserted-by":"crossref","unstructured":"Breunig, M.\u00a0M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. In SIGMOD \u201900.","DOI":"10.1145\/342009.335388"},{"key":"1822_CR13","doi-asserted-by":"crossref","unstructured":"Bucilu\u01ce, C., Caruana, R., & Niculescu-Mizil, A. (2006). Model compression. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining\u2014KDD \u201906 (pp. 535\u2013550). ACM Press.","DOI":"10.1145\/1150402.1150464"},{"key":"1822_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108846","volume":"248","author":"Y Cao","year":"2022","unstructured":"Cao, Y., Wan, Q., Shen, W., & Gao, L. (2022). Informative knowledge distillation for image anomaly segmentation. Knowledge-Based Systems, 248, 108846.","journal-title":"Knowledge-Based Systems"},{"key":"1822_CR15","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H., Misra, I., J\u00e9gou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging properties in self-supervised vision transformers. arXiv:2104.14294.","DOI":"10.1109\/ICCV48922.2021.00951"},{"issue":"2","key":"1822_CR16","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1109\/TII.2016.2641472","volume":"13","author":"D Carrera","year":"2017","unstructured":"Carrera, D., Manganini, F., Boracchi, G., & Lanzarone, E. (2017). Defect detection in SEM images of nanofibrous materials. IEEE Transactions on Industrial Informatics, 13(2), 551\u2013561.","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"1822_CR17","doi-asserted-by":"crossref","unstructured":"Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv:1901.03407.","DOI":"10.1145\/3394486.3406704"},{"key":"1822_CR18","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., & Balasubramanian, V.\u00a0N. (2017). Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. arXiv:1710.11063.","DOI":"10.1109\/WACV.2018.00097"},{"key":"1822_CR19","first-page":"89","volume":"16","author":"K Chromi\u0144ski","year":"2010","unstructured":"Chromi\u0144ski, K., & Tkacz, M. (2010). Comparison of outlier detection methods in biomedical data. Journal of Medical Informatics and Technologies, 16, 89\u201394.","journal-title":"Journal of Medical Informatics and Technologies"},{"key":"1822_CR20","unstructured":"Cohen, N., & Hoshen, Y (2020). Sub-image anomaly detection with deep pyramid correspondences. arXiv:2005.02357v3."},{"key":"1822_CR21","doi-asserted-by":"crossref","unstructured":"Croitoru, I., Bogolin, S.-V., & Leordeanu, M. (2019). Unsupervised learning of foreground object detection. arXiv:1808.04593.","DOI":"10.1007\/s11263-019-01183-3"},{"key":"1822_CR22","doi-asserted-by":"publisher","first-page":"112342","DOI":"10.1109\/ACCESS.2022.3216007","volume":"10","author":"JE de Albuquerque Filho","year":"2022","unstructured":"de Albuquerque Filho, J. E., Brand\u00e3o, L. C. P., Fernandes, B. J. T., & Maciel, A. M. A. (2022). A review of neural networks for anomaly detection. IEEE Access, 10, 112342\u2013112367.","journal-title":"IEEE Access"},{"key":"1822_CR23","doi-asserted-by":"crossref","unstructured":"Defard, T., Setkov, A., Loesch, A., & Audigier, R (2021). Padim: A patch distribution modeling framework for anomaly detection and localization. In A. Del Bimbo, R. Cucchiara, S. Sclaroff, G.\u00a0M. Farinella, T. Mei, M. Bertini, H.\u00a0J. Escalante, R. Vezzani (Eds.), Pattern recognition. ICPR international workshops and challenges, volume 12664 of Springer eBook Collection (pp. 475\u2013489). Springer.","DOI":"10.1007\/978-3-030-68799-1_35"},{"key":"1822_CR24","doi-asserted-by":"crossref","unstructured":"Deng, H., & Li, X (2022). Anomaly detection via reverse distillation from one-class embedding. CVPR. arXiv:2201.10703.","DOI":"10.1109\/CVPR52688.2022.00951"},{"key":"1822_CR25","doi-asserted-by":"crossref","unstructured":"Deng, J.,\u00a0Dong, W.,\u00a0Socher, R., Li, L.-J., Li, K., & Li, F.-F. (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248\u2013255). ISBN 1063-6919.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1822_CR26","unstructured":"Di Mattia, F., Galeone, P., de\u00a0Simoni, M., & Ghelfi, E. (2019). A survey on GANS for anomaly detection. arXiv:1906.11632"},{"key":"1822_CR27","first-page":"15","volume-title":"Out-of-distribution detection using outlier detection methods","author":"J Diers","year":"2022","unstructured":"Diers, J., & Pigorsch, C. (2022). Out-of-distribution detection using outlier detection methods (pp. 15\u201326). Springer."},{"key":"1822_CR28","unstructured":"Dinh, L., Krueger, D., & Bengio, Y (2014). Non-linear independent components estimation: Nice. arXiv:1410.8516."},{"key":"1822_CR29","doi-asserted-by":"crossref","unstructured":"Doersch, C., Gupta, A., & Efros, A.\u00a0A. (2015). Unsupervised visual representation learning by context prediction. arXiv:1505.05192.","DOI":"10.1109\/ICCV.2015.167"},{"key":"1822_CR30","unstructured":"Draelos, R.\u00a0L., & Carin, L. (2020). Use hirescam instead of grad-cam for faithful explanations of convolutional neural networks. arXiv:2011.08891."},{"issue":"3","key":"1822_CR31","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/MSP.2021.3134634","volume":"39","author":"L Ericsson","year":"2022","unstructured":"Ericsson, L., Gouk, H., Loy, C. C., & Hospedales, T. M. (2022). Self-supervised representation learning: Introduction, advances, and challenges. IEEE Signal Processing Magazine, 39(3), 42\u201362.","journal-title":"IEEE Signal Processing Magazine"},{"key":"1822_CR32","doi-asserted-by":"crossref","unstructured":"Esmaeilpour, S., Liu, B., Robertson, E., & Shu, L. (2021). Zero-shot out-of-distribution detection based on the pre-trained model clip. arXiv:2109.02748.","DOI":"10.1609\/aaai.v36i6.20610"},{"key":"1822_CR33","unstructured":"Fort, S., Ren, J., & Lakshminarayanan, B. (2021). Exploring the limits of out-of-distribution detection. arXiv:2106.03004."},{"key":"1822_CR34","doi-asserted-by":"crossref","unstructured":"Gaspar, J., Catumbela, E., Marques, B., & Freitas, A. (2011). A systematic review of outliers detection techniques in medical data\u2014preliminary study (pp. 575\u2013582). https:\/\/www.researchgate.net\/profile\/juliano-gaspar\/publication\/221334605_a_systematic_review_of_outliers_detection_techniques_in_medical_data_-_preliminary_study","DOI":"10.5220\/0003168705750582"},{"key":"1822_CR35","unstructured":"Gidaris, S., Singh, P., & Komodakis, N. (2018). Unsupervised representation learning by predicting image rotations. ICLR 2018."},{"key":"1822_CR36","unstructured":"Golan, I., & El-Yaniv, R. (2018). Deep anomaly detection using geometric transformations. Advances in Neural Information Processing Systems, 31."},{"key":"1822_CR37","doi-asserted-by":"crossref","unstructured":"Gourisaria, M.\u00a0K., Agrawal, R., Harshvardhan, G.\u00a0M., Pandey, M., & Rautaray, S.\u00a0S (2021). Application of machine learning in industry 4.0. In Machine learning: Theoretical foundations and practical applications (pp. 57\u201387). Springer.","DOI":"10.1007\/978-981-33-6518-6_4"},{"issue":"6","key":"1822_CR38","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou, J., Yu, B., Maybank, S. J., & Tao, Dacheng. (2021). Knowledge distillation: A survey. International Journal of Computer Vision, 129(6), 1789\u20131819.","journal-title":"International Journal of Computer Vision"},{"key":"1822_CR39","doi-asserted-by":"crossref","unstructured":"Gudovskiy, D., Ishizaka, S., & Kozuka, K. (2021). Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows. arXiv:2107.12571.","DOI":"10.1109\/WACV51458.2022.00188"},{"key":"1822_CR40","unstructured":"Gupta, M., Gao, J., Aggarwal, C., & Han, J. (2014). Outlier detection for temporal data: A survey. IEEE Transactions on Knowledge and Data Engineering. https:\/\/www.semanticscholar.org\/paper\/Outlier-Detection-for-Temporal-Data%3A-A-Survey-Gupta-Gao\/43d75d3a22db904d052d4c435e2d1f22be3887e0"},{"key":"1822_CR41","doi-asserted-by":"crossref","unstructured":"Hautamaki, V.,\u00a0Karkkainen, I., &\u00a0Franti, P. (2004). Outlier detection using k-nearest neighbour graph. In Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004. IEEE.","DOI":"10.1109\/ICPR.2004.1334558"},{"key":"1822_CR42","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep residual learning for image recognition. arXiv:1512.03385v1.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1822_CR43","unstructured":"Hendrycks, D., & Dietterich, T. (2019). Benchmarking neural network robustness to common corruptions and perturbations. arXiv:1903.12261"},{"key":"1822_CR44","unstructured":"Hendrycks, D., Mazeika, M., & Dietterich, T. (2018). Deep anomaly detection with outlier exposure. arXiv:1812.04606."},{"key":"1822_CR45","unstructured":"Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. NIPS 2014."},{"key":"1822_CR46","doi-asserted-by":"crossref","unstructured":"Hodge, V. J., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review.","DOI":"10.1023\/B:AIRE.0000045502.10941.a9"},{"issue":"1","key":"1822_CR47","first-page":"28","volume":"7","author":"E Hozdi\u0107","year":"2015","unstructured":"Hozdi\u0107, E. (2015). Smart factory for industry 4.0: A review. Journal of Modern Manufacturing Systems and Technology, 7(1), 28\u201335.","journal-title":"Journal of Modern Manufacturing Systems and Technology"},{"issue":"1","key":"1822_CR48","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s00371-018-1588-5","volume":"36","author":"Y Huang","year":"2020","unstructured":"Huang, Y., Qiu, C., & Yuan, K. (2020). Surface defect saliency of magnetic tile. The Visual Computer, 36(1), 85\u201396.","journal-title":"The Visual Computer"},{"issue":"1","key":"1822_CR49","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3390\/technologies9010002","volume":"9","author":"A Jaiswal","year":"2021","unstructured":"Jaiswal, A., Babu, A. R., Zadeh, M. Z., Banerjee, D., & Makedon, F. (2021). A survey on contrastive self-supervised learning. Technologies, 9(1), 2.","journal-title":"Technologies"},{"issue":"1","key":"1822_CR50","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3390\/technologies9010002","volume":"9","author":"A Jaiswal","year":"2021","unstructured":"Jaiswal, A., Babu, A. R., Zadeh, M. Z., Banerjee, D., & Makedon, F. (2021). A survey on contrastive self-supervised learning. Technologies, 9(1), 2.","journal-title":"Technologies"},{"key":"1822_CR51","doi-asserted-by":"crossref","unstructured":"Jang, J., Hwang, E., & Park, S.-H. (2022). N-pad: Neighboring pixel-based industrial anomaly detection. arXiv:2210.08768.","DOI":"10.1109\/CVPRW59228.2023.00459"},{"issue":"9\u201312","key":"1822_CR52","doi-asserted-by":"publisher","first-page":"4351","DOI":"10.1007\/s00170-017-1069-4","volume":"94","author":"CW Kang","year":"2018","unstructured":"Kang, C. W., Ramzan, M. B., Sarkar, B., & Imran, M. (2018). Effect of inspection performance in smart manufacturing system based on human quality control system. The International Journal of Advanced Manufacturing Technology, 94(9\u201312), 4351\u20134364.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"key":"1822_CR53","unstructured":"Kendall, A., & Gal, Y. (2017). What uncertainties do we need in Bayesian deep learning for computer vision? arXiv:1703.04977."},{"key":"1822_CR54","unstructured":"Kim, Y., Jang, H., Lee, D., & Choi, H.-J. (2022). Altub: Alternating training method to update base distribution of normalizing flow for anomaly detection. arXiv:2210.14913."},{"key":"1822_CR55","doi-asserted-by":"crossref","unstructured":"Kim, W., Kanezaki, A., & Tanaka, M. (2020). Unsupervised learning of image segmentation based on differentiable feature clustering. arXiv:2007.09990.","DOI":"10.1109\/TIP.2020.3011269"},{"key":"1822_CR56","unstructured":"Kim, J.-H., Kim, D.-H., Yi, S., & Lee, T. (2021). Semi-orthogonal embedding for efficient unsupervised anomaly segmentation. arXiv:2105.14737."},{"key":"1822_CR57","doi-asserted-by":"crossref","unstructured":"Kim, D., Park, C., Cho, S., & Lee, S. (2022). FAPM: Fast adaptive patch memory for real-time industrial anomaly detection. arXiv:2211.07381.","DOI":"10.1109\/ICASSP49357.2023.10096400"},{"key":"1822_CR58","doi-asserted-by":"crossref","unstructured":"Kobyzev, I., Prince, S. J. D., & Brubaker, M.\u00a0A. (2021). Normalizing flows: An introduction and review of current methods. arXiv:1908.09257.","DOI":"10.1109\/TPAMI.2020.2992934"},{"key":"1822_CR59","unstructured":"Krasin, I., Duerig, T., Alldrin, N., Ferrari, V., Abu-El-Haija, S., Kuznetsova, A., Rom, H., Uijlings, J., Popov, S., Kamali, S., Malloci, M., Pont-Tuset, J., Veit, A., Belongie, S., Gomes, V., Gupta, A., Sun, C., Chechik, G., Cai, D., Feng, Z., Narayanan, D., & Murphy, K. (2017). Openimages: A public dataset for large-scale multi-label and multi-class image classification."},{"key":"1822_CR60","doi-asserted-by":"crossref","unstructured":"Lee, S., Lee, S., & Song, B.\u00a0C. (2022). CFA: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. arXiv:2206.04325.","DOI":"10.1109\/ACCESS.2022.3193699"},{"key":"1822_CR61","doi-asserted-by":"crossref","unstructured":"Li, N., Jiang, K., Ma, Z., Wei, X., Hong, X., & Gong, Y. (2021). Anomaly detection via self-organizing map. arXiv:2107.09903.","DOI":"10.1109\/ICIP42928.2021.9506433"},{"key":"1822_CR62","doi-asserted-by":"crossref","unstructured":"Li, C.-L., Sohn, K., Yoon, J., & Pfister, T. (2021). Cutpaste: Self-supervised learning for anomaly detection and localization. arXiv:2104.04015.","DOI":"10.1109\/CVPR46437.2021.00954"},{"key":"1822_CR63","doi-asserted-by":"crossref","unstructured":"Liang, Y., Zhang, J., Zhao, S., Wu, R., Liu, Y., & Pan, S. (2022). Omni-frequency channel-selection representations for unsupervised anomaly detection. arXiv:2203.00259.","DOI":"10.1109\/TIP.2023.3293772"},{"key":"1822_CR64","doi-asserted-by":"crossref","unstructured":"Lilhore, U.\u00a0K., Simaiya, S., Kaur, A., Prasad, D., Khurana, M., Verma, D.\u00a0K., & Hassan, A. (2022). Impact of deep learning and machine learning in industry 4.0: Impact of deep learning. In V. Bali, V. Bhatnagar, D. Aggarwal, S. Bali (Eds.), Cyber-physical, IoT, and autonomous systems in industry 4.0 (pp. 179\u2013197). CRC Press. ISBN 9781003146711.","DOI":"10.1201\/9781003146711-12"},{"key":"1822_CR65","unstructured":"Liu, R., Lehman, J., Molino, P., Such, F.\u00a0P., Frank, E., Sergeev, A., & Yosinski, J. (2018). An intriguing failing of convolutional neural networks and the coordconv solution. arXiv:1807.03247."},{"key":"1822_CR66","unstructured":"Liu, T., Li, B., Zhao, Z., Xiao, D., Jiang, B., & Geng, L. (2022). Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection. arXiv:2210.14485."},{"key":"1822_CR67","doi-asserted-by":"crossref","unstructured":"Liu, W., Li, R., Zheng, M., Karanam, S., Wu, Z., Bhanu, B., Radke, R.\u00a0J., & Camps, O. (2019). Towards visually explaining variational autoencoders. arXiv:1911.07389.","DOI":"10.1109\/CVPR42600.2020.00867"},{"key":"1822_CR68","unstructured":"Liznerski, P., Ruff, L., Vandermeulen, R.\u00a0A., Franks, B.\u00a0J., Kloft, M., & M\u00fcller, K.-R. (2021). Explainable deep one-class classification. In ICLR 2021."},{"key":"1822_CR69","doi-asserted-by":"crossref","unstructured":"Madan, N., Ristea, N.-C., Ionescu, R.\u00a0T., Nasrollahi, K., Khan, F.\u00a0S., Moeslund, T.\u00a0B., & Shah, M. (2022). Self-supervised masked convolutional transformer block for anomaly detection. arXiv:2209.12148","DOI":"10.1109\/CVPR52688.2022.01321"},{"key":"1822_CR70","doi-asserted-by":"crossref","unstructured":"Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., & Terzopoulos, D. (2021). Image segmentation using deep learning: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence.","DOI":"10.1109\/TPAMI.2021.3059968"},{"key":"1822_CR71","doi-asserted-by":"crossref","unstructured":"Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., & Foresti, G.\u00a0L. (2021). VT-ADL: A vision transformer network for image anomaly detection and localization. KD-, 01\u201306. arXiv:2104.10036.","DOI":"10.1109\/ISIE45552.2021.9576231"},{"key":"1822_CR72","unstructured":"Mu, N., & Gilmer, J. (2019). Mnist-c: A robustness benchmark for computer vision. arXiv:1906.02337."},{"key":"1822_CR73","doi-asserted-by":"crossref","unstructured":"Napoletano, P., Piccoli, F., & Schettini, R. (2018). Anomaly detection in nanofibrous materials by cnn-based self-similarity. Sensors (Basel, Switzerland), 18(1).","DOI":"10.3390\/s18010209"},{"key":"1822_CR74","doi-asserted-by":"crossref","unstructured":"Noroozi, M., & Favaro, P. (2016). Unsupervised learning of visual representations by solving jigsaw puzzles. In B. Leibe, J. Matas, N. Sebe, M. Welling (Eds.), Computer vision - ECCV 2016, Lecture Notes in Computer Science (pp. 69\u201384). Springer. ISBN 978-3-319-46466-4.","DOI":"10.1007\/978-3-319-46466-4_5"},{"key":"1822_CR75","doi-asserted-by":"crossref","unstructured":"Pang, G., Shen, C., Cao, L., & den van Hengel, A. (2022). Deep learning for anomaly detection. ACM Computing Surveys, 54(2), 1\u201338. arXiv:2007.02500.","DOI":"10.1145\/3439950"},{"key":"1822_CR76","unstructured":"Radford, A., Kim, J.\u00a0W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. arXiv:2103.00020."},{"key":"1822_CR77","unstructured":"Rezende, D.\u00a0J., & Mohamed, S. (2015). Variational inference with normalizing flows. arXiv:1505.05770."},{"key":"1822_CR78","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.\u00a0T., Singh, S., & Guestrin, C. (2016). Why should I trust you? Explaining the predictions of any classifier. arXiv:1602.04938.","DOI":"10.1145\/2939672.2939778"},{"key":"1822_CR79","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.\u00a0T., Singh, S., & Guestrin, C. (2018). Anchors: High-precision model-agnostic explanations. In AAAI conference on artificial intelligence (AAAI) (pp. 1\u20139).","DOI":"10.1609\/aaai.v32i1.11491"},{"key":"1822_CR80","doi-asserted-by":"crossref","unstructured":"Rippel, O., Mertens, P., & Merhof, D. (2020). Modeling the distribution of normal data in pre-trained deep features for anomaly detection. arXiv:2005.14140.","DOI":"10.1109\/TIM.2021.3098381"},{"issue":"13","key":"1822_CR81","doi-asserted-by":"publisher","first-page":"4750","DOI":"10.3390\/s22134750","volume":"22","author":"O Rippel","year":"2022","unstructured":"Rippel, O., Zwinge, C., & Merhof, D. (2022). Increasing the generalization of supervised fabric anomaly detection methods to unseen fabrics. Sensors, 22(13), 4750.","journal-title":"Sensors"},{"key":"1822_CR82","doi-asserted-by":"crossref","unstructured":"Ristea, N.-C., Madan, N., Ionescu, R.\u00a0T., Nasrollahi, K., Khan, F.\u00a0S., Moeslund, T.\u00a0B., & Shah, M. (2021). Self-supervised predictive convolutional attentive block for anomaly detection. arXiv:2111.09099.","DOI":"10.1109\/CVPR52688.2022.01321"},{"key":"1822_CR83","doi-asserted-by":"crossref","unstructured":"Roth, K., Pemula, L., Zepeda, J., Sch\u00f6lkopf, B., Brox, T., & Gehler, P. (2021). Towards total recall in industrial anomaly detection. arXiv:2106.08265v1.","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"1822_CR84","doi-asserted-by":"crossref","unstructured":"Rudolph, M., Wandt, B., & Rosenhahn, B. (2020). Same same but differnet: Semi-supervised defect detection with normalizing flows. arXiv:2008.12577.","DOI":"10.1109\/WACV48630.2021.00195"},{"key":"1822_CR85","doi-asserted-by":"crossref","unstructured":"Rudolph, M., Wehrbein, T., Rosenhahn, B., & Wandt, B. (2021). Fully convolutional cross-scale-flows for image-based defect detection. arXiv:2110.02855.","DOI":"10.1109\/WACV51458.2022.00189"},{"key":"1822_CR86","doi-asserted-by":"crossref","unstructured":"Rudolph, M., Wehrbein, T., Rosenhahn, B., & Wandt, B. (2022). Asymmetric student-teacher networks for industrial anomaly detection. arXiv:2210.07829.","DOI":"10.1109\/WACV56688.2023.00262"},{"key":"1822_CR87","doi-asserted-by":"crossref","unstructured":"Ruff, L., Kauffmann, J.\u00a0R., Vandermeulen, R.\u00a0A., Montavon, G., Samek, W., Kloft, M., Dietterich, T.\u00a0G., & Muller, K.-R. (2021). A unifying review of deep and shallow anomaly detection. Proceedings of the IEEE, 109(5), 756\u2013795. arXiv:2009.11732.","DOI":"10.1109\/JPROC.2021.3052449"},{"key":"1822_CR88","unstructured":"Ruff, L., Vandermeulen, R.\u00a0A., Franks, B.\u00a0J., M\u00fcller, K.-R., & Kloft, M. (2020). Rethinking assumptions in deep anomaly detection. arXiv:2006.00339."},{"key":"1822_CR89","unstructured":"Ruff, L., Vandermeulen, R.\u00a0A., Franks, B.\u00a0J., M\u00fcller, K.-R., & Kloft, M. (2020a). Rethinking assumptions in deep anomaly detection. arXiv:2006.00339."},{"key":"1822_CR90","unstructured":"Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Ahmed, S., Siddiqui, A., Binder, A., M\u00fcller, E., & Kloft, M. (2018). Deep one-class classification. In International conference on machine learning (pp. 4393\u20134402). https:\/\/proceedings.mlr.press\/v80\/ruff18a.html"},{"key":"1822_CR91","unstructured":"Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S. A., Binder, A., M\u00fcller, E., & Kloft, M. (2018). Deep one-class classification. In International conference on machine learning (pp. 4393\u20134402). https:\/\/proceedings.mlr.press\/v80\/ruff18a.html"},{"key":"1822_CR92","unstructured":"Ruff, L., Vandermeulen, R.\u00a0A., G\u00f6rnitz, N., Binder, A., M\u00fcller, E., M\u00fcller, K.-R., & Kloft, M. (2020b). Deep semi-supervised anomaly detection."},{"key":"1822_CR93","unstructured":"Schl\u00fcter, H.\u00a0M., Tan, J., Hou, B., & Kainz, B. (2021). Self-supervised out-of-distribution detection and localization with natural synthetic anomalies (NSA). arXiv:2109.15222."},{"key":"1822_CR94","unstructured":"Sch\u00f6lkopf, B., Williamson, R.\u00a0C.,\u00a0Smola, A.,\u00a0Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection."},{"key":"1822_CR95","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.\u00a0R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-cam: Visual explanations from deep networks via gradient-based localization. In 2017 IEEE international conference on computer vision (ICCV) (pp. 618\u2013626). IEEE.","DOI":"10.1109\/ICCV.2017.74"},{"key":"1822_CR96","doi-asserted-by":"crossref","unstructured":"Shahid, N., Naqvi, I., & Qaisar, Saad B. (2012). Characteristics and classification of outlier detection techniques for wireless sensor networks in Harsh environments: A survey. Artificial Intelligence Review.","DOI":"10.1007\/s10462-012-9370-y"},{"issue":"1","key":"1822_CR97","first-page":"307","volume":"9","author":"K Singh","year":"2012","unstructured":"Singh, K., & Shuchita, U. (2012). Outlier detection: Applications and techniques. International Journal of Computer Science Issues (IJCSI), 9(1), 307.","journal-title":"International Journal of Computer Science Issues (IJCSI)"},{"key":"1822_CR98","unstructured":"Song, J., Kong, K., Park, Y.-I., Kim, S.-G., & Kang, S.-J. (2021). AnoSeg: Anomaly segmentation network using self-supervised learning. https:\/\/www.researchgate.net\/publication\/355141739_AnoSeg_Anomaly_Segmentation_Network_Using_Self-Supervised_Learning"},{"key":"1822_CR99","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1016\/j.apsusc.2013.09.002","volume":"285","author":"K Song","year":"2013","unstructured":"Song, K., & Yan, Y. (2013). A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Applied Surface Science, 285, 858\u2013864.","journal-title":"Applied Surface Science"},{"key":"1822_CR100","doi-asserted-by":"crossref","unstructured":"Tabernik, D., \u0160ela, S., Skvar\u010d, J., & Sko\u010daj, D. (2020). Segmentation-based deep-learning approach for surface-defect detection. arXiv:1903.08536.","DOI":"10.1007\/s10845-019-01476-x"},{"issue":"2","key":"1822_CR101","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3312739","volume":"52","author":"A Taha","year":"2020","unstructured":"Taha, A., & Hadi, A. S. (2020). Anomaly detection methods for categorical data. ACM Computing Surveys, 52(2), 1\u201335.","journal-title":"ACM Computing Surveys"},{"key":"1822_CR102","unstructured":"Tan, M., & Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning. arXiv:1905.11946v5."},{"issue":"12","key":"1822_CR103","doi-asserted-by":"publisher","first-page":"3336","DOI":"10.3390\/s20123336","volume":"20","author":"T-W Tang","year":"2020","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. Sensors, 20(12), 3336.","journal-title":"Sensors"},{"issue":"1","key":"1822_CR104","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","volume":"54","author":"DMJ Tax","year":"2004","unstructured":"Tax, D. M. J., & Duin, R. P. W. (2004). Support vector data description. Machine Learning, 54(1), 45\u201366.","journal-title":"Machine Learning"},{"key":"1822_CR105","doi-asserted-by":"crossref","unstructured":"Thakkar, P., Vala, J. B., & Prajapati, V. (2016). Survey on outlier detection in data stream. International Journal of Computer Applications.","DOI":"10.5120\/ijca2016908257"},{"key":"1822_CR106","doi-asserted-by":"crossref","unstructured":"Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., & J\u00e9gou, H. (2021). Going deeper with image transformers. arXiv:2103.17239.","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"1822_CR107","doi-asserted-by":"crossref","unstructured":"Tsai, C.\u00a0C., Wu, T.\u00a0H, & Lai, S.\u00a0H. (2022). Multi-scale patch-based representation learning for image anomaly detection and segmentation. In 2022 IEEE\/CVF winter conference on applications of computer vision (WACV) (pp. 3065\u20133073). ISBN 2642-9381.","DOI":"10.1109\/WACV51458.2022.00312"},{"key":"1822_CR108","doi-asserted-by":"crossref","unstructured":"Wan, Q., YunKang, C., Gao, L., Weiming, S., & Li, X. (2022c). Position encoding enhanced feature mapping for image anomaly detection.","DOI":"10.1109\/CASE49997.2022.9926547"},{"key":"1822_CR109","unstructured":"Wang, X., Girdhar, R., Yu, S. X., & Misra, I. (2023). Cut and learn for unsupervised object detection and instance segmentation. arXiv:2301.11320."},{"key":"1822_CR110","unstructured":"Wang, G., Han, S., Ding, E., & Huang, D. (2021a). Student-teacher feature pyramid matching for anomaly detection. arXiv:2103.04257."},{"key":"1822_CR111","unstructured":"Wang, Y., Shen, X., Yuan, Y., Yuming, D., Li, M., Hu, S. X., Crowley, J.\u00a0L., & Vaufreydaz, D. (2022). Tokencut: Segmenting objects in images and videos with self-supervised transformer and normalized cut. arXiv:2209.00383."},{"key":"1822_CR112","doi-asserted-by":"crossref","unstructured":"Wang, S., Wu, L., Cui, L., & Shen, Y. (2021b). Glancing at the patch: Anomaly localization with global and local feature comparison. In 2021 IEEE\/CVF conference on computer vision and pattern recognition (CVPR) (pp. 254\u2013263). IEEE. ISBN 978-1-6654-4509-2.","DOI":"10.1109\/CVPR46437.2021.00032"},{"issue":"6","key":"1822_CR113","doi-asserted-by":"publisher","first-page":"6182","DOI":"10.1109\/TIE.2021.3094452","volume":"69","author":"Q Wan","year":"2022","unstructured":"Wan, Q., Gao, L., Li, X., & Wen, L. (2022). Industrial image anomaly localization based on gaussian clustering of pretrained feature. IEEE Transactions on Industrial Electronics, 69(6), 6182\u20136192.","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"1822_CR114","first-page":"1","volume":"25","author":"Q Wan","year":"2022","unstructured":"Wan, Q., Gao, L., Li, X., & Wen, L. (2022). Unsupervised image anomaly detection and segmentation based on pre-trained feature mapping. IEEE Transactions on Industrial Informatics, 25, 1\u201310.","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"4","key":"1822_CR115","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600\u2013612.","journal-title":"IEEE Transactions on Image Processing"},{"key":"1822_CR116","unstructured":"Wieler, M., & Hahn, T. (2007). Weakly supervised learning for industrial optical inspection."},{"key":"1822_CR117","unstructured":"Xia, X., & Kulis, B (2017). W-Net: A deep model for fully unsupervised image segmentation. arXiv:1711.08506"},{"key":"1822_CR118","unstructured":"Xu, H., Caramanis, C., & Sanghavi, S. (2010). Robust PCA via outlier pursuit. Advances in Neural Information Processing Systems, 23."},{"key":"1822_CR119","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1007\/978-3-030-32236-6_51","volume":"25","author":"F Xu","year":"2019","unstructured":"Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., & Zhu, J. (2019). Explainable AI: A brief survey on history, research areas, approaches and challenges. Lecture Notes in Computer Science, 25, 563\u2013574.","journal-title":"Lecture Notes in Computer Science"},{"key":"1822_CR120","unstructured":"Yamada, S., & Hotta, K (2021). Reconstruction student with attention for student-teacher pyramid matching. arXiv:2111.15376."},{"key":"1822_CR121","doi-asserted-by":"crossref","unstructured":"Yamada, S., Kamiya, S., & Hotta, K. (2022). Reconstructed student-teacher and discriminative networks for anomaly detection. arXiv:2210.07548.","DOI":"10.1109\/IROS47612.2022.9981509"},{"key":"1822_CR122","doi-asserted-by":"crossref","unstructured":"Yang, M., Wu, P., Liu, J., & Feng, H. (2022). MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities. arXiv:2205.00908.","DOI":"10.1016\/j.engappai.2023.105835"},{"key":"1822_CR123","unstructured":"Yi, J., & Yoon, S. (2020). Patch SVDD: Patch-level svdd for anomaly detection and segmentation. arXiv:2006.16067."},{"key":"1822_CR124","unstructured":"Yu, J., Wang, Z., Vasudevan, V., Yeung, L., Seyedhosseini, M., & Wu, Y. (2022). Coca: Contrastive captioners are image-text foundation models. arXiv:2205.01917."},{"key":"1822_CR125","unstructured":"Yu, J.,\u00a0Zheng, Y., Wang, X., Li, W., Wu, Y., Zhao, R., & Wu, L. (2021). Fastflow: Unsupervised anomaly detection and localization via 2D normalizing flows. arXiv:2111.07677."},{"key":"1822_CR126","doi-asserted-by":"crossref","unstructured":"Zavrtanik, V., Kristan, M., & Sko\u010daj, D. (2021). Draem: A discriminatively trained reconstruction embedding for surface anomaly detection. arXiv:2108.07610.","DOI":"10.1109\/ICCV48922.2021.00822"},{"key":"1822_CR127","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Wang, X., Deng, R., Bao, T., Zhao, R., & Wu, L. (2021). Focus your distribution: Coarse-to-fine non-contrastive learning for anomaly detection and localization. arXiv:2110.04538.","DOI":"10.1109\/ICME52920.2022.9859925"},{"key":"1822_CR128","unstructured":"Zheng, Y., Wang, X., Qi, Y., Li, W., & Wu, L. (2022). Benchmarking unsupervised anomaly detection and localization."},{"issue":"1","key":"1822_CR129","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.arcontrol.2010.02.008","volume":"34","author":"D Zuehlke","year":"2010","unstructured":"Zuehlke, D. (2010). Smartfactory\u2014towards a factory-of-things. Annual Reviews in Control, 34(1), 129\u2013138.","journal-title":"Annual Reviews in Control"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-023-01822-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-023-01822-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-023-01822-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T02:25:10Z","timestamp":1729563910000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-023-01822-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,10]]},"references-count":129,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["1822"],"URL":"https:\/\/doi.org\/10.1007\/s11263-023-01822-w","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,10]]},"assertion":[{"value":"18 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}