{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T23:54:31Z","timestamp":1783554871432,"version":"3.55.0"},"reference-count":182,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001602","name":"Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality","doi-asserted-by":"publisher","award":["18\/CRT\/6224"],"award-info":[{"award-number":["18\/CRT\/6224"]}],"id":[{"id":"10.13039\/501100001602","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3165574","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T19:26:05Z","timestamp":1649359565000},"page":"39045-39068","source":"Crossref","is-referenced-by-count":24,"title":["Skin Disease Analysis With Limited Data in Particular Rosacea: A Review and Recommended Framework"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9975-8705","authenticated-orcid":false,"given":"Anwesha","family":"Mohanty","sequence":"first","affiliation":[{"name":"School of Computing, Dublin City University, Dublin 9, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alistair","family":"Sutherland","sequence":"additional","affiliation":[{"name":"School of Computing, Dublin City University, Dublin 9, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9366-5113","authenticated-orcid":false,"given":"Marija","family":"Bezbradica","sequence":"additional","affiliation":[{"name":"School of Computing, Dublin City University, Dublin 9, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5640-4798","authenticated-orcid":false,"given":"Hossein","family":"Javidnia","sequence":"additional","affiliation":[{"name":"School of Computing, Dublin City University, Dublin 9, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/jid.2013.446"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s13671-017-0192-7"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1111\/bjd.19704"},{"key":"ref4","volume-title":"European Dermatology Health Care Survey 2013 Short Report","author":"Augustin","year":"2013"},{"key":"ref5","volume-title":"How Can Dermatology Services Meet Current and Future Patient Needs, While Ensuring Quality of Care is Not Compromised and Access is Equitable Across the UK?","year":"2014"},{"key":"ref6","volume-title":"Dermatology Report","year":"2014"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1177\/1203475420914619"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1001\/jamadermatol.2016.5411"},{"key":"ref9","volume-title":"Dermatology 2016 Facesheet","year":"2016"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1097\/CM9.0000000000000389"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.5114\/ada.2020.94834"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1177\/2150132720937831"},{"key":"ref13","volume-title":"Dermnetnz","year":"2021"},{"issue":"8","key":"ref14","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1056\/NEJMcp042829","article-title":"Rosacea","volume":"352","author":"Powell","year":"2005","journal-title":"New England J. Med."},{"issue":"6","key":"ref15","first-page":"694","article-title":"Why is rosacea considered to be an inflammatory disorder? The primary role, clinical relevance, and therapeutic correlations of abnormal innate immune response in rosacea-prone skin","volume":"11","author":"Rosso","year":"2012","journal-title":"J. Drugs Dermatol."},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaad.2013.04.045"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1111\/bjd.15780"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1111\/bjd.20485"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1111\/bjd.16481"},{"key":"ref20","volume-title":"Red Skin & Rashes are Not Always the Result of Rosacea","year":"2021"},{"key":"ref21","volume-title":"The National Rosacea Society has Conducted Surveys of Rosacea Sufferers to Offer Insights Into the Effect on Social Life","year":"2021"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2133.2012.11037.x"},{"key":"ref23","volume-title":"Rosacea Affects More Than 3 Million Canadians","year":"2021"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1159\/000246214"},{"key":"ref25","volume-title":"Rosacea Treatment Market Size, Share & Trends Analysis Report by Drug Class (Alpha Agonists, Antibiotics, Retinoids, Corticosteroids), by Mode of Administration (Topical, Oral), by Region, and Segment Forecasts, 2019\u20132025, Report ID: GVR-2-68038-739-1","year":"2019"},{"key":"ref26","volume-title":"2021 Rosacea Report","year":"2021"},{"key":"ref27","volume-title":"Treatments for Rosacea","year":"2021"},{"key":"ref28","volume-title":"Rosacea: What is it and How Can You Manage it?","year":"2021"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2020.574329"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.2196\/23415"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.21037\/atm.2020.04.39"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2021.626369"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104458"},{"key":"ref37","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017","journal-title":"arXiv:1704.04861"},{"key":"ref38","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104118"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref42","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tan"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1111\/srt.12726"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1111\/srt.12817"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33642-4_3"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref49","volume-title":"The MNIST Database of Handwritten Digits","author":"LeCun","year":"1998"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.18152"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1806905115"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2018.11.015"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2837502"},{"key":"ref55","article-title":"Semi-supervised few-shot learning for medical image segmentation","author":"Feyjie","year":"2020","journal-title":"arXiv:2003.08462"},{"key":"ref56","article-title":"Ensemble model with batch spectral regularization and data blending for cross-domain few-shot learning with unlabeled data","author":"Zhao","year":"2020","journal-title":"arXiv:2006.04323"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbx044"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0192-5"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32248-9_45"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2824327"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.jid.2018.01.028"},{"key":"ref62","volume-title":"Dermatology Atlas Brazil","year":"2021"},{"key":"ref63","volume-title":"An Atlas of Clinical Dermatology","year":"2021"},{"key":"ref64","volume-title":"DermIS","year":"2021"},{"key":"ref65","volume-title":"DermNet Skin Disease Atlas","year":"2021"},{"key":"ref66","volume-title":"Dermofit Image Library","year":"2021"},{"key":"ref67","volume-title":"Dermato Web Spain","year":"2021"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.161"},{"key":"ref69","volume-title":"Hellenic Dermatological Atlas","year":"2021"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-021-00815-z"},{"key":"ref71","volume-title":"ISIC Archive","year":"2021"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.04.034"},{"key":"ref73","volume-title":"Molemap NewZealand","year":"2021"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2017.7950681"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2013.6610779"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_13"},{"key":"ref77","volume-title":"SD198","year":"2021"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/323268"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1001\/archderm.139.3.361"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1158\/1078-0432.CCR-03-0039"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(02)00679-4"},{"key":"ref82","article-title":"Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC)","author":"Gutman","year":"2016","journal-title":"arXiv:1605.01397"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1097\/00008390-199806000-00009"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/3413.001.0001"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1148\/rg.2017160130"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2553401"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2017.12.028"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.3348\/kjr.2017.18.4.570"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1007\/s12194-017-0406-5"},{"key":"ref90","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"ref91","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"25","author":"Krizhevsky"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2016.36"},{"key":"ref94","article-title":"NIPS 2016 tutorial: Generative adversarial networks","author":"Goodfellow","year":"2017","journal-title":"arXiv:1701.00160"},{"key":"ref95","first-page":"1","article-title":"Generative adversarial nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"27","author":"Goodfellow"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2020.00164"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101938"},{"key":"ref98","volume-title":"Meta-Learning: Learning to Learn Fast","author":"Weng","year":"2018"},{"key":"ref99","volume-title":"Learning to Learn With Gradients","author":"Finn","year":"2018"},{"key":"ref100","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1145\/311535.311556"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1145\/3395208"},{"key":"ref103","first-page":"223","article-title":"An introduction to active shape models","volume-title":"Image Processing and Analysis","volume":"328","author":"Cootes","year":"2000"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1117\/12.431093"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.5244\/C.21.79"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2739743"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.117"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.163"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2954885"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00125"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3059336"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-0842-3"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32040-9_25"},{"key":"ref116","article-title":"Automated skin lesion classification using ensemble of deep neural networks in ISIC 2018: Skin lesion analysis towards melanoma detection challenge","author":"Milton","year":"2019","journal-title":"arXiv:1901.10802"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaad.2019.06.042"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0193321"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1109\/MMAR.2017.8046978"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.2316\/P.2017.852-053"},{"key":"ref121","doi-asserted-by":"publisher","DOI":"10.1016\/j.tice.2019.04.009"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104115"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01155"},{"key":"ref124","article-title":"Supervised classification of dermatological diseases by deep learning","author":"Mishra","year":"2018","journal-title":"arXiv:1802.03752"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00137"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1109\/JCSSE.2019.8864155"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.33969\/AIS.2020.21006"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0217293"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8683352"},{"key":"ref130","article-title":"Skin lesions classification using convolutional neural networks in clinical images","author":"Mendes","year":"2018","journal-title":"arXiv:1812.02316"},{"key":"ref131","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105568"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2019.8857905"},{"key":"ref133","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"Radford","year":"2015","journal-title":"arXiv:1511.06434"},{"key":"ref134","article-title":"Deep generative image models using a Laplacian pyramid of adversarial networks","author":"Denton","year":"2015","journal-title":"arXiv:1506.05751"},{"key":"ref135","article-title":"Conditional generative adversarial nets","author":"Mirza","year":"2014","journal-title":"arXiv:1411.1784"},{"key":"ref136","article-title":"Progressive growing of GANs for improved quality, stability, and variation","author":"Karras","year":"2017","journal-title":"arXiv:1710.10196"},{"key":"ref137","article-title":"MelanoGANs: High resolution skin lesion synthesis with GANs","author":"Baur","year":"2018","journal-title":"arXiv:1804.04338"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01201-4_28"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101716"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00330"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01201-4_32"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-7717-y"},{"key":"ref143","first-page":"155","article-title":"DermGAN: Synthetic generation of clinical skin images with pathology","volume-title":"Proc. Mach. Learn. Health Workshop","author":"Ghorbani"},{"issue":"1","key":"ref144","article-title":"Synthesizing skin lesion images using CycleGANs\u2014A case study","volume-title":"Proc. Norsk IKT-Konferanse Forskning Utdanning","author":"Fossen-Romsaas"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00204"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00917"},{"key":"ref149","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00244"},{"key":"ref150","article-title":"Using latent space regression to analyze and leverage compositionality in GANs","author":"Chai","year":"2021","journal-title":"arXiv:2103.10426"},{"key":"ref151","article-title":"Training generative adversarial networks with limited data","author":"Karras","year":"2020","journal-title":"arXiv:2006.06676"},{"key":"ref152","first-page":"2554","article-title":"Meta networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Munkhdalai"},{"key":"ref153","first-page":"1842","article-title":"Meta-learning with memory-augmented neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Santoro"},{"key":"ref154","article-title":"A simple neural attentive meta-learner","author":"Mishra","year":"2017","journal-title":"arXiv:1707.03141"},{"key":"ref155","first-page":"3630","article-title":"Matching networks for one shot learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"29","author":"Vinyals"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref157","article-title":"Few-shot learning with graph neural networks","author":"Garcia","year":"2017","journal-title":"arXiv:1711.04043"},{"key":"ref158","article-title":"Prototypical networks for few-shot learning","author":"Snell","year":"2017","journal-title":"arXiv:1703.05175"},{"key":"ref159","article-title":"On first-order meta-learning algorithms","author":"Nichol","year":"2018","journal-title":"arXiv:1803.02999"},{"key":"ref160","article-title":"Auto-meta: Automated gradient based meta learner search","author":"Kim","year":"2018","journal-title":"arXiv:1806.06927"},{"key":"ref161","article-title":"Generalized inner loop meta-learning","author":"Grefenstette","year":"2019","journal-title":"arXiv:1910.01727"},{"key":"ref162","article-title":"Meta-learning with adaptive hyperparameters","author":"Baik","year":"2020","journal-title":"arXiv:2011.00209"},{"key":"ref163","first-page":"8535","article-title":"Data augmentation using learned transforms for one-shot medical image segmentation","volume":"abs\/1902.09383","author":"Zhao","year":"2019","journal-title":"CoRR"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58583-9_8"},{"key":"ref165","article-title":"Bayesian meta-learning for the few-shot setting via deep kernels","author":"Patacchiola","year":"2019","journal-title":"arXiv:1910.05199"},{"key":"ref166","article-title":"SB-MTL: Score-based meta transfer-learning for cross-domain few-shot learning","author":"Cai","year":"2020","journal-title":"arXiv:2012.01784"},{"key":"ref167","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00049"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00815"},{"key":"ref169","article-title":"Meta-learning in neural networks: A survey","author":"Hospedales","year":"2020","journal-title":"arXiv:2004.05439"},{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.1145\/3386252"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_35"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00373"},{"key":"ref173","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM49941.2020.9313372"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780198505709.001.0001"},{"key":"ref175","article-title":"Gaussian process morphable models for spatially-varying multi-scale registration","author":"Gerig","year":"2021"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.1007\/s13555-020-00461-0"},{"key":"ref177","doi-asserted-by":"publisher","DOI":"10.1007\/s10103-020-03200-1"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00874"},{"key":"ref179","article-title":"MeshGAN: Non-linear 3D morphable models of faces","author":"Cheng","year":"2019","journal-title":"arXiv:1903.10384"},{"key":"ref180","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01329-8"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3084524"},{"key":"ref182","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2021.7249"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09751076.pdf?arnumber=9751076","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T00:20:58Z","timestamp":1705537258000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9751076\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":182,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3165574","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}