{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:11:22Z","timestamp":1760177482173,"version":"build-2065373602"},"reference-count":75,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,5,29]],"date-time":"2020-05-29T00:00:00Z","timestamp":1590710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Image based instance recognition is a difficult problem, in some cases even for the human eye. While latest developments in computer vision\u2014mostly driven by deep learning\u2014have shown that high performance models for classification or categorization can be engineered, the problem of discriminating similar objects with a low number of samples remain challenging. Advances from multi-class classification are applied for object matching problems, as the feature extraction techniques are the same; nature-inspired multi-layered convolutional nets learn the representations, and the output of such a model maps them to a multidimensional encoding space. A metric based loss brings same instance embeddings close to each other. While these solutions achieve high classification performance, low efficiency is caused by memory cost of high parameter number, which is in a relationship with input image size. Upon shrinking the input, the model requires less trainable parameters, while performance decreases. This drawback is tackled by using compressed feature extraction, e.g., projections. In this paper, a multi-directional image projection transformation with fixed vector lengths (MDIPFL) is applied for one-shot recognition tasks, trained on Siamese and Triplet architectures. Results show, that MDIPFL based approach achieves decent performance, despite of the significantly lower number of parameters.<\/jats:p>","DOI":"10.3390\/a13060133","type":"journal-article","created":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T11:49:21Z","timestamp":1591012161000},"page":"133","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Metric Embedding Learning on Multi-Directional Projections"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8845-8301","authenticated-orcid":false,"given":"G\u00e1bor","family":"Kert\u00e9sz","sequence":"first","affiliation":[{"name":"John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, B\u00e9csi \u00fat 96b, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_3","unstructured":"Bromley, J., Guyon, I., LeCun, Y., S\u00e4ckinger, E., and Shah, R. (December, January 28). Signature verification using a \u201csiamese\u201d time delay neural network. Proceedings of the Advances in Neural Information Processing Systems, Denver, CO, USA."},{"key":"ref_4","unstructured":"Koch, G., Zemel, R., and Salakhutdinov, R. (2015, January 10\u201311). Siamese neural networks for one-shot image recognition. Proceedings of the ICML Deep Learning Workshop, Lille, France."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., and Philbin, J. (2015, January 7\u201312). Facenet: A unified embedding for face recognition and clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref_6","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, Y., Huang, H., Xie, Q., Yao, L., and Chen, Q. (2018). Research on a surface defect detection algorithm based on MobileNet-SSD. Appl. Sci., 8.","DOI":"10.3390\/app8091678"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ribeiro, M., Grolinger, K., and Capretz, M.A. (2015, January 9\u201311). Mlaas: Machine learning as a service. Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA.","DOI":"10.1109\/ICMLA.2015.152"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/MNET.2018.1700202","article-title":"Learning IoT in edge: Deep learning for the Internet of Things with edge computing","volume":"32","author":"Li","year":"2018","journal-title":"IEEE Netw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gaidhane, V., Singh, V., and Kumar, M. (July, January 29). Image compression using PCA and improved technique with MLP neural network. Proceedings of the 2010 International Conference on Advances in Recent Technologies in Communication and Computing, Bradford, UK.","DOI":"10.1109\/ARTCom.2010.15"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dev, S., Wen, B., Lee, Y.H., and Winkler, S. (2016). Machine learning techniques and applications for ground-based image analysis. arXiv.","DOI":"10.1109\/MGRS.2015.2510448"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1002\/aic.690370209","article-title":"Nonlinear principal component analysis using autoassociative neural networks","volume":"37","author":"Kramer","year":"1991","journal-title":"AIChE J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cui, W., Zhou, Q., and Zheng, Z. (2018). Application of a hybrid model based on a convolutional auto-encoder and convolutional neural network in object-oriented remote sensing classification. Algorithms, 11.","DOI":"10.3390\/a11010009"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Deng, C., Lin, W., Lee, B.s., and Lau, C.T. (2010, January 19\u201323). Robust image compression based on compressive sensing. Proceedings of the 2010 IEEE International Conference on Multimedia and Expo, Singapore.","DOI":"10.1109\/ICME.2010.5583387"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kert\u00e9sz, G., Sz\u00e9n\u00e1si, S., and Vamossy, Z. (July, January 30). A novel method for robust multi-directional image projection computation. Proceedings of the 2016 IEEE 20th Jubilee International Conference on Intelligent Engineering Systems (INES), Budapest, Hungary.","DOI":"10.1109\/INES.2016.7555128"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"100183","DOI":"10.1016\/j.imu.2019.100183","article-title":"Robust medical image compression based on wavelet transform and vector quantization","volume":"15","author":"Ammah","year":"2019","journal-title":"Inform. Med. Unlocked"},{"key":"ref_19","first-page":"211","article-title":"Multi-Directional Image Projections with Fixed Resolution for Object Matching","volume":"15","year":"2018","journal-title":"Acta Polytech. Hung."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation applied to handwritten zip code recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_21","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NA, USA."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_23","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_24","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bengio, Y. (2012). Practical recommendations for gradient-based training of deep architectures. Neural Networks: Tricks of the Trade, Springer.","DOI":"10.1007\/978-3-642-35289-8_26"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hua, G., and J\u00e9gou, H. (2016). Fully-convolutional siamese networks for object tracking. Computer Vision\u2014ECCV 2016 Workshops, Springer.","DOI":"10.1007\/978-3-319-46604-0"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/978-3-319-46484-8_48","article-title":"Gated siamese convolutional neural network architecture for human re-identification","volume":"Volume 9912","author":"Leibe","year":"2016","journal-title":"Computer Vision\u2014ECCV 2016"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., and Wolf, L. (2014, January 23\u201328). Deepface: Closing the gap to human-level performance in face verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.220"},{"key":"ref_29","unstructured":"Hadsell, R., Chopra, S., and LeCun, Y. (2006, January 17\u201322). Dimensionality reduction by learning an invariant mapping. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), New York, NY, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, J., Song, Y., Leung, T., Rosenberg, C., Wang, J., Philbin, J., Chen, B., and Wu, Y. (2014, January 23\u201328). Learning fine-grained image similarity with deep ranking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.180"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Gupta, A., and Girshick, R. (2016, January 27\u201330). Training region-based object detectors with online hard example mining. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.89"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wu, C.Y., Manmatha, R., Smola, A.J., and Krahenbuhl, P. (2017, January 22\u201329). Sampling matters in deep embedding learning. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.309"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1109\/TIFS.2017.2765524","article-title":"Image to video person re-identification by learning heterogeneous dictionary pair with feature projection matrix","volume":"13","author":"Zhu","year":"2017","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_34","first-page":"2","article-title":"Openface: A general-purpose face recognition library with mobile applications","volume":"6","author":"Amos","year":"2016","journal-title":"CMU Sch. Comput. Sci."},{"key":"ref_35","unstructured":"Hermans, A., Beyer, L., and Leibe, B. (2017). In defense of the triplet loss for person re-identification. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xuan, H., Stylianou, A., and Pless, R. (2020, January 1\u20135). Improved embeddings with easy positive triplet mining. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Snowmass, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093432"},{"key":"ref_37","unstructured":"Radon, J. (2020, May 14). Uber die Bestimmung von Funktionen durch ihre Integralwerte l\u00e4ngs gewissez Mannigfaltigheiten. Available online: https:\/\/www.bibsonomy.org\/bibtex\/1fdcf4d741dfedba3c56a3a942ecc463b\/haggis79."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1109\/TMI.1986.4307775","article-title":"On the determination of functions from their integral values along certain manifolds","volume":"5","author":"Radon","year":"1986","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_39","unstructured":"Hough, P.V. (1962). Method and Means for Recognizing Complex Patterns. (3,069,654), US Patent."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1145\/361237.361242","article-title":"Use of the Hough transformation to detect lines and curves in pictures","volume":"15","author":"Duda","year":"1972","journal-title":"Commun. ACM"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ramlau, R., and Scherzer, O. (2019). The Radon Transform: The First 100 Years and Beyond, Walter de Gruyter GmbH & Co KG.","DOI":"10.1515\/9783110560855"},{"key":"ref_42","unstructured":"Deans, S. (1983). The Radon Transform and Some of Its Applications, John Wiley and Sons."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s001380050126","article-title":"Real-time multiple vehicle detection and tracking from a moving vehicle","volume":"12","author":"Betke","year":"2000","journal-title":"Mach. Vis. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Liu, Y., Collins, R., and Tsin, Y. (2002, January 23\u201328). Gait sequence analysis using frieze patterns. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/3-540-47967-8_44"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1016\/j.imavis.2013.06.007","article-title":"Vehicle matching in smart camera networks using image projection profiles at multiple instances","volume":"31","author":"Philips","year":"2013","journal-title":"Image Vis. Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1109\/JPROC.2008.928742","article-title":"An introduction to distributed smart cameras","volume":"96","author":"Rinner","year":"2008","journal-title":"Proc. IEEE"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kawamura, A., Yoshimitsu, Y., Kajitani, K., Naito, T., Fujimura, K., and Kamijo, S. (2011, January 9\u201312). Smart camera network system for use in railway stations. Proceedings of the 2011 IEEE International Conference on Systems, Man, and Cybernetics, Anchorage, AK, USA.","DOI":"10.1109\/ICSMC.2011.6083647"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Alessandrelli, D., Azzar\u00e0, A., Petracca, M., Nastasi, C., and Pagano, P. (2012, January 13\u201315). ScanTraffic: Smart camera network for traffic information collection. Proceedings of the European Conference on Wireless Sensor Networks, Trento, Italy.","DOI":"10.1007\/978-3-642-28169-3_13"},{"key":"ref_49","unstructured":"Grother, P., and Hanaoka, K. (2016). NIST Special Database 19 Handprinted Forms and Characters, National Institute of Standards and Technology. [2nd ed.]. Tech. Report."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kert\u00e9sz, G., Sz\u00e9n\u00e1si, S., and V\u00e1mossy, Z. (2017, January 26\u201328). Application and properties of the Radon transform for object image matching. Proceedings of the SAMI 2017, Herl\u2019any, Slovakia.","DOI":"10.1109\/SAMI.2017.7880333"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.cma.2018.03.024","article-title":"Using multiple graphics accelerators to solve the two-dimensional inverse heat conduction problem","volume":"336","author":"Felde","year":"2018","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhang, X., and Lan, X. (2017, January 22\u201329). How to train triplet networks with 100k identities?. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.225"},{"key":"ref_53","unstructured":"Li, C., Ma, X., Jiang, B., Li, X., Zhang, X., Liu, X., Cao, Y., Kannan, A., and Zhu, Z. (2017). Deep speaker: An end-to-end neural speaker embedding system. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Yagfarov, R., Ostankovich, V., and Akhmetzyanov, A. (2020, January 23\u201325). Traffic Sign Classification Using Embedding Learning Approach for Self-driving Cars. Human Interaction, Emerging Technologies and Future Applications II. Proceedings of the 2nd International Conference on Human Interaction and Emerging Technologies: Future Applications (IHIET\u2013AI 2020), Lausanne, Switzerland.","DOI":"10.1007\/978-3-030-44267-5_27"},{"key":"ref_55","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An introduction to kernel and nearest-neighbor nonparametric regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Stat."},{"key":"ref_57","unstructured":"Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., and Weinberger, K.Q. (2013). One-shot learning by inverting a compositional causal process. Advances in Neural Information Processing Systems 26, Curran Associates, Inc."},{"key":"ref_58","first-page":"34","article-title":"The MNIST database of handwritten digits, 1998","volume":"10","author":"LeCun","year":"1998","journal-title":"J. Intel. Learn. Syst. Appl."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Cohen, G., Afshar, S., Tapson, J., and Van Schaik, A. (2017, January 14\u201319). EMNIST: Extending MNIST to handwritten letters. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref_60","unstructured":"Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., and Han, J. (2019). On the variance of the adaptive learning rate and beyond. arXiv."},{"key":"ref_61","unstructured":"Chollet, F. (2018). Keras: The Python Deep Learning Library, Astrophysics Source Code Library."},{"key":"ref_62","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, Savannah, GA, USA."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/CVPR.2005.202","article-title":"Learning a similarity metric discriminatively, with application to face verification","volume":"Volume 1","author":"Chopra","year":"2005","journal-title":"Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905)"},{"key":"ref_64","unstructured":"Almazan, J., Gajic, B., Murray, N., and Larlus, D. (2018). Re-id done right: Towards good practices for person re-identification. arXiv."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Kuma, R., Weill, E., Aghdasi, F., and Sriram, P. (2019, January 14\u201319). Vehicle re-identification: An efficient baseline using triplet embedding. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852059"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Zapletal, D., and Herout, A. (July, January 26). Vehicle Re-Identification for Automatic Video Traffic Surveillance. Proceedings of the the IEEE Conference on Computer Vision and Pattern Recognition Workshops 2016, Las Vegas, NV, USA.","DOI":"10.1109\/CVPRW.2016.195"},{"key":"ref_67","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., and Le, Q.V. (2018, January 18\u201322). Learning transferable architectures for scalable image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref_69","unstructured":"Real, E., Aggarwal, A., Huang, Y., and Le, Q.V. (February, January 27). Regularized evolution for image classifier architecture search. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q.V. (2019, January 15\u201321). Mnasnet: Platform-aware neural architecture search for mobile. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00293"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Mateen, M., Wen, J., Song, S., and Huang, Z. (2019). Fundus image classification using VGG-19 architecture with PCA and SVD. Symmetry, 11.","DOI":"10.3390\/sym11010001"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"3010","DOI":"10.1007\/s11227-017-2216-2","article-title":"The individual identification method of wireless device based on dimensionality reduction and machine learning","volume":"75","author":"Lin","year":"2019","journal-title":"J. Supercomput."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Maeda, E. (2020). Dimensionality reduction. Computer Vision: A Reference Guide, Springer.","DOI":"10.1007\/978-3-030-03243-2_652-1"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Sz\u00e9n\u00e1si, S., V\u00e1mossy, Z., and Kozlovszky, M. (2012, January 5). Preparing initial population of genetic algorithm for region growing parameter optimization. Proceedings of the 2012 4th IEEE International Symposium on Logistics and Industrial Informatics, Smolenice, Slovakia.","DOI":"10.1109\/LINDI.2012.6319460"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Sun, Q., Liu, Y., Chua, T.S., and Schiele, B. (2019, January 15\u201321). Meta-transfer learning for few-shot learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00049"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/6\/133\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:33:41Z","timestamp":1760175221000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/6\/133"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,29]]},"references-count":75,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["a13060133"],"URL":"https:\/\/doi.org\/10.3390\/a13060133","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2020,5,29]]}}}