{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T12:55:15Z","timestamp":1780491315292,"version":"3.54.1"},"reference-count":137,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T00:00:00Z","timestamp":1625011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Foundation Ireland","award":["13\/RC\/2094"],"award-info":[{"award-number":["13\/RC\/2094"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.<\/jats:p>","DOI":"10.3390\/s21134486","type":"journal-article","created":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T02:44:39Z","timestamp":1625107479000},"page":"4486","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Representation Learning for Fine-Grained Change Detection"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4087-3987","authenticated-orcid":false,"given":"Niall","family":"O\u2019Mahony","sequence":"first","affiliation":[{"name":"Lero\u2014The Irish Software Research Centre, V92 CX88 Tralee, Ireland"},{"name":"Department of Agricultural and Manufacturing Engineering, School of Science Technology Engineering and Maths (STEM), Kerry Campus, Munster Technological University, V92 CX88 Tralee, Ireland"},{"name":"IMaR Research Centre, Kerry Campus, Munster Technological University, V92 CX88 Tralee, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sean","family":"Campbell","sequence":"additional","affiliation":[{"name":"Lero\u2014The Irish Software Research Centre, V92 CX88 Tralee, Ireland"},{"name":"Department of Agricultural and Manufacturing Engineering, School of Science Technology Engineering and Maths (STEM), Kerry Campus, Munster Technological University, V92 CX88 Tralee, Ireland"},{"name":"IMaR Research Centre, Kerry Campus, Munster Technological University, V92 CX88 Tralee, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lenka","family":"Krpalkova","sequence":"additional","affiliation":[{"name":"Lero\u2014The Irish Software Research Centre, V92 CX88 Tralee, Ireland"},{"name":"Department of Agricultural and Manufacturing Engineering, School of Science Technology Engineering and Maths (STEM), Kerry Campus, Munster Technological University, V92 CX88 Tralee, Ireland"},{"name":"IMaR Research Centre, Kerry Campus, Munster Technological University, V92 CX88 Tralee, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anderson","family":"Carvalho","sequence":"additional","affiliation":[{"name":"Lero\u2014The Irish Software Research Centre, V92 CX88 Tralee, Ireland"},{"name":"Department of Agricultural and Manufacturing Engineering, School of Science Technology Engineering and Maths (STEM), Kerry Campus, Munster Technological University, V92 CX88 Tralee, Ireland"},{"name":"IMaR Research Centre, Kerry Campus, Munster Technological University, V92 CX88 Tralee, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6756-3700","authenticated-orcid":false,"given":"Joseph","family":"Walsh","sequence":"additional","affiliation":[{"name":"Lero\u2014The Irish Software Research Centre, V92 CX88 Tralee, Ireland"},{"name":"Department of Agricultural and Manufacturing Engineering, School of Science Technology Engineering and Maths (STEM), Kerry Campus, Munster Technological University, V92 CX88 Tralee, Ireland"},{"name":"IMaR Research Centre, Kerry Campus, Munster Technological University, V92 CX88 Tralee, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Riordan","sequence":"additional","affiliation":[{"name":"Lero\u2014The Irish Software Research Centre, V92 CX88 Tralee, Ireland"},{"name":"Department of Agricultural and Manufacturing Engineering, School of Science Technology Engineering and Maths (STEM), Kerry Campus, Munster Technological University, V92 CX88 Tralee, Ireland"},{"name":"IMaR Research Centre, Kerry Campus, Munster Technological University, V92 CX88 Tralee, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-020-0255-1","article-title":"Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging","volume":"3","author":"Li","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_2","unstructured":"Wei, X.S., Wu, J., and Cui, Q. (2019). Deep learning for fine-grained image analysis: A survey. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"703","DOI":"10.5194\/isprs-archives-XLIII-B2-2020-703-2020","article-title":"Current challenges in operational very high resolution land-cover mapping","volume":"43","author":"Mallet","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"16","DOI":"10.3389\/fenvs.2014.00016","article-title":"Data assimilation: Making sense of Earth Observation","volume":"2","author":"Lahoz","year":"2014","journal-title":"Front. Environ. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Paolanti, M., Pietrini, R., Mancini, A., Frontoni, E., and Zingaretti, P. (2020). Deep understanding of shopper behaviours and interactions using RGB-D vision. Mach. Vis. Appl.","DOI":"10.1007\/s00138-020-01118-w"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Guerrero-Ib\u00e1\u00f1ez, J., Zeadally, S., and Contreras-Castillo, J. (2018). Sensor technologies for intelligent transportation systems. Sensors, 18.","DOI":"10.3390\/s18041212"},{"key":"ref_7","first-page":"37","article-title":"Multi-sensor anomalous change detection at scale","volume":"Volume 10986","author":"Messinger","year":"2019","journal-title":"Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Awty-Carroll, K., Bunting, P., Hardy, A., and Bell, G. (2019). An Evaluation and Comparison of Four Dense Time Series Change Detection Methods Using Simulated Data. Remote Sens., 11.","DOI":"10.3390\/rs11232779"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.eswa.2017.12.038","article-title":"MSIM: A change detection framework for damage assessment in natural disasters","volume":"97","author":"Qin","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shi, W., Zhang, M., Zhang, R., Chen, S., and Zhan, Z. (2020). Change detection based on artificial intelligence: State-of-the-art and challenges. Remote Sens., 12.","DOI":"10.3390\/rs12101688"},{"key":"ref_11","unstructured":"Senanayake, R., Ott, L., O\u2019Callaghan, S., and Ramos, F. (2016, January 16\u201321). Spatio-temporal hilbert maps for continuous occupancy representation in dynamic environments. Proceedings of the 30th International Conference on Neural Information Processing Systems, Kyoto, Japan."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Foody, G.M., and Atkinson, P.M. (2002). Uncertainty in Remote Sensing and GIS, John Wiley & Sons, Inc.","DOI":"10.1002\/0470035269"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.isprsjprs.2016.09.013","article-title":"3D change detection\u2014Approaches and applications","volume":"122","author":"Qin","year":"2016","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1016\/j.patcog.2012.10.027","article-title":"Quantitative error measures for edge detection","volume":"46","author":"Bustince","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2061","DOI":"10.1007\/s12652-019-01232-2","article-title":"An improved industrial sub-pixel edge detection algorithm based on coarse and precise location","volume":"11","author":"Xie","year":"2020","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_16","unstructured":"Tao, J., Turjo, M., and Tan, Y.P. (2006, January 21\u201324). Quickest change detection for health-care video surveillance. Proceedings of the 2006 IEEE International Symposium on Circuits and Systems, Kos, Greece."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.imavis.2016.01.006","article-title":"Violence detection using Oriented VIolent Flows","volume":"48","author":"Gao","year":"2016","journal-title":"Image Vis. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kataoka, H., Satoh, Y., Aoki, Y., Oikawa, S., and Matsui, Y. (2018). Temporal and fine-grained pedestrian action recognition on driving recorder database. Sensors, 18.","DOI":"10.3390\/s18020627"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"13531","DOI":"10.1109\/ACCESS.2017.2714258","article-title":"A Continuous Change Detection Mechanism to Identify Anomalies in ECG Signals for WBAN-Based Healthcare Environments","volume":"5","author":"Member","year":"2017","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.artmed.2015.12.001","article-title":"SmartFABER: Recognizing Fine-grained Abnormal Behaviors for Early Detection of Mild Cognitive Impairment","volume":"67","author":"Riboni","year":"2016","journal-title":"Artif. Intell. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.jbi.2016.07.020","article-title":"Unsupervised detection and analysis of changes in everyday physical activity data","volume":"63","author":"Sprint","year":"2016","journal-title":"J. Biomed. Inform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1049\/htl.2015.0062","article-title":"Robust cardiac event change detection method for long-term healthcare monitoring applications","volume":"3","author":"Satija","year":"2016","journal-title":"Healthc. Technol. Lett."},{"key":"ref_23","unstructured":"Colt, R.G., V\u00e1rady, C.H., Volpi, R., and Malag\u00f2, L. (2021). Automatic Feature Extraction for Heartbeat Anomaly Detection. arXiv."},{"key":"ref_24","first-page":"197","article-title":"Ontology versioning and change detection on the web","volume":"Volume 2473","author":"Klein","year":"2002","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1016\/j.ymssp.2017.07.007","article-title":"Improved Tactile Resonance Sensor for Robotic Assisted Surgery David","volume":"99","author":"Uribe","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_26","first-page":"126","article-title":"Change-point detection method for clinical decision support system rule monitoring","volume":"Volume 10259","author":"Liu","year":"2017","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Borg, M., De La Vara, J.L., and Wnuk, K. (2016). Practitioners\u2019 Perspectives on Change Impact  Analysis for Safety-Critical Software\u2014A Preliminary Analysis. International Conference on Computer Safety, Reliability, and Security, Springer.","DOI":"10.1007\/978-3-319-45480-1_28"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.ifacol.2019.09.178","article-title":"Passive Fault Tolerant Control System Using Feed-forward Neural Network for Two-Tank Interacting Conical Level Control System against Partial Actuator Failures and Disturbances","volume":"Volume 52","author":"Patel","year":"2019","journal-title":"IFAC-PapersOnLine"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kelly, A. (2013). Mobile Robotics, Cambridge University Press.","DOI":"10.1017\/CBO9781139381284"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1177\/1932296817719089","article-title":"Autoregressive Modeling of Drift and Random Error to Characterize a Continuous Intravascular Glucose Monitoring Sensor","volume":"12","author":"Zhou","year":"2018","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_31","first-page":"3","article-title":"STL: A seasonal-trend decomposition","volume":"6","author":"Cleveland","year":"1990","journal-title":"J. Off. Stat."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1080\/01621459.2012.737745","article-title":"Optimal detection of changepoints with a linear computational cost","volume":"107","author":"Killick","year":"2012","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_33","unstructured":"Aoga, J. (2018). Global Constraints for Mining Sets and Sequences. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1214\/aos\/1176324466","article-title":"Using the Generalized Likelihood Ratio Statistic for Sequential Detection of a Change-Point","volume":"23","author":"Siegmund","year":"1995","journal-title":"Ann. Stat."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1109\/18.979323","article-title":"Online activity detection in a multiuser environment using the matrix CUSUM algorithm","volume":"48","author":"Oskiper","year":"2002","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1288","DOI":"10.1016\/j.jss.2010.02.006","article-title":"A cusum change-point detection algorithm for non-stationary sequences with application to data network surveillance","volume":"83","author":"Jeske","year":"2010","journal-title":"J. Syst. Softw."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3336","DOI":"10.1002\/sim.6547","article-title":"Dynamic probability control limits for risk-adjusted Bernoulli CUSUM charts","volume":"34","author":"Zhang","year":"2015","journal-title":"Stat. Med."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"O\u2019 Mahony, N., Campbell, S., Carvalho, A., Harapanahalli, S., Velasco Hernandez, G., Krpalkova, L., Riordan, D., and Walsh, J. (2019). Deep Learning vs. Traditional Computer Vision. Advances in Computer Vision, Springer. Chapter Deep Learn.","DOI":"10.1007\/978-3-030-17795-9_10"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"G\u00f3rska, U., Rupp, A., Boubenec, Y., Celikel, T., and Englitz, B. (2018). Evidence integration in natural acoustic textures during active and passive listening. eNeuro, 5.","DOI":"10.1523\/ENEURO.0090-18.2018"},{"key":"ref_40","unstructured":"Bardsiri, A.K., and Hashemi, S.M. (2019). Computer Vision for 3D Perception A Review. Intelligent Systems and Applications, Springer. [869th ed.]. Chapter 59."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","article-title":"A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications","volume":"30","author":"Cai","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Dai, Y., Wang, S., Xiong, N.N., and Guo, W. (2020). A Survey on Knowledge Graph Embedding: Approaches, Applications and Benchmarks. Electronics, 9.","DOI":"10.3390\/electronics9050750"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Dong, H., Ma, W., Wu, Y., Zhang, J., and Jiao, L. (2020). Self-Supervised Representation Learning for Remote Sensing Image Change Detection Based on Temporal Prediction. Remote Sens., 12.","DOI":"10.3390\/rs12111868"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Lim, S.K., Loo, Y., Tran, N.T., Cheung, N.M., Roig, G., and Elovici, Y. (2018, January 17\u201320). DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN. Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM), Singapore.","DOI":"10.1109\/ICDM.2018.00146"},{"key":"ref_45","unstructured":"Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., and Lillicrap, T. (2016). One-shot Learning with Memory-Augmented Neural Networks. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neunet.2019.01.012","article-title":"Continual Lifelong Learning with Neural Networks: A Review","volume":"113","author":"Parisi","year":"2019","journal-title":"Neural Netw."},{"key":"ref_47","first-page":"681","article-title":"A Metric Learning Reality Check","volume":"Volume 12370","author":"Musgrave","year":"2020","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.promfg.2020.01.025","article-title":"One-shot learning for custom identification tasks; A review","volume":"Volume 38","author":"Campbell","year":"2019","journal-title":"Procedia Manufacturing"},{"key":"ref_49","unstructured":"Manmatha, R., Wu, C.Y., 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."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ifacol.2019.12.406","article-title":"Generative Adversarial Network Based Image Augmentation for Insect Pest Classification Enhancement","volume":"52","author":"Lu","year":"2019","journal-title":"IFAC-PapersOnLine"},{"key":"ref_51","unstructured":"Zhang, K., Luo, W., Zhong, Y., Ma, L., Liu, W., and Li, H. (2018). Adversarial Spatio-Temporal Learning for Video Deblurring. arXiv."},{"key":"ref_52","unstructured":"Li, Y., Zhao, W., Wang, H., Sung, M., and Guibas, L. (2018). GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud. arXiv."},{"key":"ref_53","unstructured":"Mehrotra, A., and Dukkipati, A. (2017). Generative Adversarial Residual Pairwise Networks for One Shot Learning. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.neucom.2019.06.041","article-title":"Label-Removed Generative Adversarial Networks Incorporating with K-Means","volume":"361","author":"Wang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_55","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016). Improved Techniques for Training GANs. Advances in Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Kingma, D.P., and Welling, M. (2019). An introduction to variational autoencoders. arXiv.","DOI":"10.1561\/9781680836233"},{"key":"ref_57","unstructured":"Fortuin, V., H\u00fcser, M., Locatello, F., Strathmann, H., and R\u00e4tsch, G. (2018). SOM-VAE: Interpretable Discrete Representation Learning on Time Series. arXiv."},{"key":"ref_58","unstructured":"Hudson, D.A., and Zitnick, C.L. (2021). Generative Adversarial Transformers. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Girdhar, R., Carreira, J., Doersch, C., and Zisserman, A. (2019, January 16\u201320). Video Action Transformer Network. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2019, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00033"},{"key":"ref_60","unstructured":"Barla, N. (2021, March 05). Understanding Representation Learning with Autoencoder: Everything You Need to Know about Representation and Feature Learning\u2014neptune.ai. Available online: https:\/\/neptune.ai\/blog\/understanding-representation-learning-with-autoencoder-everything-you-need-to-know-about-representation-and-feature-learning."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","article-title":"Generative Adversarial Networks: An Overview","volume":"35","author":"Creswell","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Grigorescu, S. (2018, January 21\u201325). Generative One-Shot Learning (GOL): A Semi-Parametric Approach to One-Shot Learning in Autonomous Vision. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8461174"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Minervini, P., Demeester, T., Rockt\u00e4schel, T., and Riedel, S. (2017). Adversarial Sets for Regularising Neural Link Predictors. arXiv.","DOI":"10.18653\/v1\/K18-1007"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MSP.2017.2693418","article-title":"Geometric deep learning: Going beyond Euclidean data","volume":"34","author":"Bronstein","year":"2017","journal-title":"IEEE Sig. Proc. Mag."},{"key":"ref_65","unstructured":"Hamilton, W.L., Ying, R., and Leskovec, J. (2017). Representation Learning on Graphs: Methods and Applications. arXiv."},{"key":"ref_66","unstructured":"Bronstein, A.M. (2011). Spectral descriptors for deformable shapes. arXiv."},{"key":"ref_67","unstructured":"Garcia, V., and Bruna, J. (2017). Few-Shot Learning with Graph Neural Networks. arXiv."},{"key":"ref_68","unstructured":"Zhu, W., and Razavian, N. (2019). Graph Neural Network on Electronic Health Records for Predicting Alzheimer\u2019s Disease. arXiv."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Zhang, R., Hao, Y., Yu, D., Chang, W.C., Lai, G., and Yang, Y. (2020). Correlation-aware Unsupervised Change-point Detection via Graph Neural Networks. arXiv.","DOI":"10.1007\/978-3-030-63836-8_46"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1450","DOI":"10.1109\/JSTARS.2020.2982631","article-title":"Unsupervised Change Detection Analysis in Satellite Image Time Series Using Deep Learning Combined with Graph-Based Approaches","volume":"13","author":"Kalinicheva","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Murphy, C., Laurence, E., and Allard, A. (2020). Deep learning of stochastic contagion dynamics on complex networks. arXiv.","DOI":"10.21203\/rs.3.rs-36564\/v1"},{"key":"ref_72","unstructured":"Garofalo, M., Pellegrino, M.A., Altabba, A., and Cochez, M. (2018). Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases. arXiv."},{"key":"ref_73","first-page":"e2","article-title":"How to Use t-SNE Effectively","volume":"1","author":"Wattenberg","year":"2017","journal-title":"Distill"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1111\/cgf.13672","article-title":"Latent Space Cartography: Visual Analysis of Vector Space Embeddings","volume":"38","author":"Liu","year":"2019","journal-title":"Comput. Graph. Forum"},{"key":"ref_75","unstructured":"Frenzel, M.F., Teleaga, B., and Ushio, A. (2019). Latent Space Cartography: Generalised Metric-Inspired Measures and Measure-Based Transformations for Generative Models. arXiv."},{"key":"ref_76","unstructured":"Recanatesi, S., Farrell, M., Lajoie, G., Deneve, S., Rigotti, M., and Shea-Brown, E. (2018). Predictive learning extracts latent space representations from sensory observations. bioRxiv."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.1038\/s41587-019-0336-3","article-title":"Visualizing structure and transitions in high-dimensional biological data","volume":"37","author":"Moon","year":"2019","journal-title":"Nat. Biotechnol."},{"key":"ref_78","unstructured":"Han, S.W. (2010). Efficient Change Detection Methods for Bio and Healthcare Surveillance, Georgia Institute of Technology."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Fan, H., Zhang, X., Mei, S., Chen, K., and Chen, X. (2020). M2gsnet: Multi-modal multi-task graph spatiotemporal network for ultra-short-term wind farm cluster power prediction. Appl. Sci., 10.","DOI":"10.3390\/app10217915"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Yang, Q. (2017). A Survey on Multi-Task Learning. arXiv.","DOI":"10.1093\/nsr\/nwx105"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"102783","DOI":"10.1016\/j.cviu.2019.07.003","article-title":"Multitask learning for large-scale semantic change detection","volume":"187","author":"Boulch","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1109\/TPAMI.2016.2544314","article-title":"Algorithm-Dependent Generalization Bounds for Multi-Task Learning","volume":"39","author":"Liu","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_83","unstructured":"Garg, S., and Liang, Y. (2020). Functional Regularization for Representation Learning: A Unified Theoretical Perspective. arXiv."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"775","DOI":"10.3233\/IFS-2012-0597","article-title":"Kernel regression with sparse metric learning","volume":"24","author":"Huang","year":"2013","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_85","unstructured":"Capela, F., Nouchi, V., Van Deursen, R., Tetko, I.V., and Godin, G. (2019). Multitask Learning on Graph Neural Networks Applied to Molecular Property Predictions. arXiv."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.1007\/s00180-020-00965-5","article-title":"Regression and subgroup detection for heterogeneous samples","volume":"35","author":"Liang","year":"2020","journal-title":"Comput. Stat."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zhang, X., Huang, J., Wang, H., and Xin, Q. (2020). Fine-Grained Building Change Detection From Very High-Spatial-Resolution Remote Sensing Images Based on Deep Multitask Learning. IEEE Geosci. Remote Sens. Lett., 1\u20135.","DOI":"10.1109\/LGRS.2020.3018858"},{"key":"ref_88","unstructured":"Wang, L., and Zhu, D. (2019). Tackling multiple ordinal regression problems: Sparse and deep multi-task learning approaches. arXiv."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1007\/s10994-013-5379-y","article-title":"Geometry preserving multi-task metric learning","volume":"92","author":"Yang","year":"2013","journal-title":"Mach. Learn."},{"key":"ref_90","unstructured":"Mathieu, E., Le Lan, C., Maddison, C.J., Tomioka, R., and Whye Teh, Y. (2019). Continuous Hierarchical Representations with Poincar\u00e9 Variational AutoEncoders. arXiv."},{"key":"ref_91","first-page":"428","article-title":"The Geometry of Continuous Latent Space Models for Network Data","volume":"34","author":"Smith","year":"2019","journal-title":"Stat. Sci. Rev. J. Inst. Math. Stat."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.1016\/j.jvcir.2014.08.006","article-title":"Multi-manifold metric learning for face recognition based on image sets","volume":"25","author":"Huang","year":"2014","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Jain, P. (2018). Unsupervised Metric Learning Using Low Dimensional Embedding. Preprints.","DOI":"10.20944\/preprints201809.0197.v1"},{"key":"ref_94","unstructured":"Perrault-Joncas, D., and Melia, M. (2012). Metric Learning and Manifolds: Preserving the Intrinsic Geometry, University of Washington."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Li, Y. (2017). Curvature-aware Manifold Learning. arXiv.","DOI":"10.1016\/j.patcog.2018.06.007"},{"key":"ref_96","unstructured":"Dutta, U.K., Harandi, M., and Sekhar, C.C. (2020). Affinity Guided Geometric Semi-Supervised Metric Learning. arXiv."},{"key":"ref_97","unstructured":"Sapienza, F., Groisman, P., and Jonckheere, M. (2021, June 12). Weighted Geodesic Distance Following Fermat\u2019s Principle. Available online: https:\/\/openreview.net\/forum?id=BJfaMIJwG."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1007\/s11063-019-10000-4","article-title":"Hessian Regularized Distance Metric Learning for People Re-Identification","volume":"50","author":"Feng","year":"2019","journal-title":"Neural Process. Lett."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation Learning: A Review and New Perspectives","volume":"35","author":"Bengio","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Tang, J., Shao, Z., and Ma, L. (2020, January 6\u201310). Fine-Grained Expression Manipulation Via Structured Latent Space. Proceedings of the 2020 IEEE International Conference on Multimedia and Expo (ICME), London, UK.","DOI":"10.1109\/ICME46284.2020.9102852"},{"key":"ref_101","unstructured":"Tschannen, M., Djolonga, J., Rubenstein, P.K., Gelly, S., and Lucic, M. (2019). On Mutual Information Maximization for Rep-Resentation Learning. arXiv."},{"key":"ref_102","unstructured":"Chen, P., Jia, T., Wu, P., Wu, J., and Chen, D. (2019). Learning Deep Representations by Mutual Information for Person Re-identification. arXiv."},{"key":"ref_103","unstructured":"Hjelm, D., and Bachman, P. (2020). Representation Learning with Video Deep InfoMax. arXiv."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Liu, L., Li, X., Cheung, W.K., and Xu, C. (2018). A Structural Representation Learning for Multi-relational Networks. arXiv.","DOI":"10.24963\/ijcai.2017\/565"},{"key":"ref_105","unstructured":"Veli\u010dkovi\u0107, P., Fedus, W., Hamilton, W.L., Li\u00f2, P., Bengioy, Y., and Hjelm, R.D. (2018). Deep graph infomax. arXiv."},{"key":"ref_106","unstructured":"Wang, Z., Zhang, Y., Zhang, Y., Jiang, J., Yang, R., Zhao, J., and Xia, G. (2020). Pianotree Vae: Structured Representation Learning for Polyphonic Music. arXiv."},{"key":"ref_107","unstructured":"Nakka, K.K., and Salzmann, M. (2018). Deep Attentional Structured Representation Learning for Visual Recognition. arXiv."},{"key":"ref_108","unstructured":"Finn, C., Abbeel, P., and Levine, S. (2017, January 6\u201311). Lifelong Few-Shot Learning. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Finman, R., Whelan, T., Kaess, M., and Leonard, J.J. (2013, January 25\u201327). Toward lifelong object segmentation from change detection in dense RGB-D maps. Proceedings of the 2013 European Conference on Mobile Robots (ECMR 2013), Barcelona, Spain.","DOI":"10.1109\/ECMR.2013.6698839"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1111\/j.1365-2869.2004.00433.x","article-title":"Circadian activity rhythm in demented and non-demented nursing-home residents measured by telemetric actigraphy","volume":"14","author":"Paavilainen","year":"2005","journal-title":"J. Sleep Res."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/TITB.2012.2196439","article-title":"Activity density map visualization and dissimilarity comparison for eldercare monitoring","volume":"16","author":"Wang","year":"2012","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_112","unstructured":"Weinberger, K.Q., and Tesauro, G. (2007, January 21\u201324). Metric Learning for Kernel Regression. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, AISTATS 2007, San Juan, Puerto Rico."},{"key":"ref_113","unstructured":"Taha, A., Chen, Y.T., Misu, T., Shrivastava, A., and Davis, L. (2019). Unsupervised data uncertainty learning in visual retrieval systems. arXiv."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Shi, Y., Bellet, A., and Sha, F. (2014). Sparse Compositional Metric Learning. arXiv.","DOI":"10.1609\/aaai.v28i1.8968"},{"key":"ref_115","unstructured":"Ying, Y., Huang, K., and Campbell, C. (2009, January 7\u201310). Sparse Metric Learning via Smooth Optimization. Proceedings of the 22nd International Conference on Neural Information Processing Systems (NIPS\u201909), Vancouver, BC, Canada."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Aiordachioaie, D., and Popescu, T.D. (2018, January 28\u201330). Change Detection by Feature Extraction and Processing from Time-Frequency Images. Proceedings of the 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI 2018), Iasi, Romania.","DOI":"10.1109\/ECAI.2018.8678973"},{"key":"ref_117","unstructured":"Hajij, M., Zamzmi, G., and Cai, X. (2021). Persistent Homology and Graphs Representation Learning. arXiv."},{"key":"ref_118","first-page":"47","article-title":"A User\u2019s Guide to Topological Data Analysis","volume":"4","author":"Munch","year":"2017","journal-title":"J. Learn. Anal."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Bouchaffra, D., and Ykhlef, F. (2021). Persistent Homology for Land Cover Change Detection. Oxford Research Encyclopedia of Natural Hazard Science, Oxford University Press.","DOI":"10.1093\/acrefore\/9780199389407.013.366"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Hajij, M., Wang, B., Scheidegger, C., and Rosen, P. (2018, January 10\u201313). Visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology. Proceedings of the IEEE Pacific Visualization Symposium, Kobe, Japan.","DOI":"10.1109\/PacificVis.2018.00024"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1109\/TMI.2015.2416271","article-title":"Persistent Homology in Sparse Regression and its Application to Brain Morphometry","volume":"34","author":"Chung","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_122","first-page":"148","article-title":"Unsupervised change analysis using supervised learning","volume":"Volume 5012","author":"Hido","year":"2008","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s10115-016-0987-z","article-title":"A survey of methods for time series change point detection","volume":"51","author":"Aminikhanghahi","year":"2017","journal-title":"Knowl. Inf. Syst."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10661-017-6120-2","article-title":"Land use and land cover (LULC) of the Republic of the Maldives: First national map and LULC change analysis using remote-sensing data","volume":"189","author":"Fallati","year":"2017","journal-title":"Environ. Monit. Assess."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","volume":"128","author":"Selvaraju","year":"2016","journal-title":"Int. J. Comput. Vis."},{"key":"ref_126","unstructured":"Shi, S., Zhang, X., and Fan, W. (2020). A Modified Perturbed Sampling Method for Local Interpretable Model-agnostic Explanation. arXiv."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/978-3-030-55180-3_8","article-title":"Understanding and Exploiting Dependent Variables with Deep Metric Learning","volume":"Volume 1250","author":"Campbell","year":"2021","journal-title":"Advances in Intelligent Systems and Computing"},{"key":"ref_128","unstructured":"Zhu, S., Yang, T., and Chen, C. (2019). Visual Explanation for Deep Metric Learning. arXiv."},{"key":"ref_129","unstructured":"Verma, S., Dickerson, J., and Hines, K. (2020). Counterfactual Explanations for Machine Learning: A Review. arXiv."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1109\/JPROC.2021.3058954","article-title":"Towards Causal Representation Learning","volume":"109","author":"Locatello","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_131","unstructured":"Yao, L., Li, S., Li, Y., Huai, M., Gao, J., and Zhang, A. (2018). Representation Learning for Treatment Effect Estimation from Observational Data. Advances in Neural Information Processing Systems 31 (NeurIPS 2018), Curran Associates Inc."},{"key":"ref_132","unstructured":"Borghesi, A., Baldo, F., and Milano, M. (2020). Improving Deep Learning Models via Constraint-Based Domain Knowledge: A Brief Survey. arXiv."},{"key":"ref_133","unstructured":"Seo, S., and Liu, Y. (2019). Differentiable Physics-informed Graph Networks. arXiv."},{"key":"ref_134","unstructured":"Cranmer, M., Greydanus, S., Hoyer, S., Research, G., Battaglia, P., Spergel, D., and Ho, S. (2020). Lagrangian Neural Networks. arXiv."},{"key":"ref_135","unstructured":"Greydanus, S., Dzamba, M., and Yosinski, J. (2019). Hamiltonian Neural Networks. arXiv."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Rockt\u00e4schel, T., Singh, S., and Riedel, S. Injecting Logical Background Knowledge into Embeddings for Relation Extraction. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.","DOI":"10.3115\/v1\/N15-1118"},{"key":"ref_137","unstructured":"Gsponer, S., Costabello, L., Van, C.L., Pai, S., Gueret, C., Ifrim, G., and Lecue, F. (2020). Background Knowledge Injection for Interpretable Sequence Classification. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4486\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:23:57Z","timestamp":1760163837000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4486"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,30]]},"references-count":137,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21134486"],"URL":"https:\/\/doi.org\/10.3390\/s21134486","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,30]]}}}