{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T09:45:20Z","timestamp":1770889520632,"version":"3.50.1"},"reference-count":69,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T00:00:00Z","timestamp":1620000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Over the past decade, deep learning has achieved unprecedented successes in a diversity of application domains, given large-scale datasets. However, particular domains, such as healthcare, inherently suffer from data paucity and imbalance. Moreover, datasets could be largely inaccessible due to privacy concerns, or lack of data-sharing incentives. Such challenges have attached significance to the application of generative modeling and data augmentation in that domain. In this context, this study explores a machine learning-based approach for generating synthetic eye-tracking data. We explore a novel application of variational autoencoders (VAEs) in this regard. More specifically, a VAE model is trained to generate an image-based representation of the eye-tracking output, so-called scanpaths. Overall, our results validate that the VAE model could generate a plausible output from a limited dataset. Finally, it is empirically demonstrated that such approach could be employed as a mechanism for data augmentation to improve the performance in classification tasks.<\/jats:p>","DOI":"10.3390\/jimaging7050083","type":"journal-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T11:06:01Z","timestamp":1620212761000},"page":"83","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Variational Autoencoder for Image-Based Augmentation of Eye-Tracking Data"],"prefix":"10.3390","volume":"7","author":[{"given":"Mahmoud","family":"Elbattah","sequence":"first","affiliation":[{"name":"Laboratoire Mod\u00e9lisation, Information, Syst\u00e8mes (MIS), Universit\u00e9 de Picardie Jules Verne, 80080 Amiens, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Colm","family":"Loughnane","sequence":"additional","affiliation":[{"name":"Faculty of Science and Engineering, University of Limerick, V94 T9PX Limerick, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Luc","family":"Gu\u00e9rin","sequence":"additional","affiliation":[{"name":"Laboratoire Mod\u00e9lisation, Information, Syst\u00e8mes (MIS), Universit\u00e9 de Picardie Jules Verne, 80080 Amiens, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Romuald","family":"Carette","sequence":"additional","affiliation":[{"name":"Laboratoire Mod\u00e9lisation, Information, Syst\u00e8mes (MIS), Universit\u00e9 de Picardie Jules Verne, 80080 Amiens, France"},{"name":"Evolucare Technologies, 80800 Villers-Bretonneux, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Federica","family":"Cilia","sequence":"additional","affiliation":[{"name":"Laboratoire CRP-CPO, Universit\u00e9 de Picardie Jules Verne, 80000 Amiens, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7497-1182","authenticated-orcid":false,"given":"Gilles","family":"Dequen","sequence":"additional","affiliation":[{"name":"Laboratoire Mod\u00e9lisation, Information, Syst\u00e8mes (MIS), Universit\u00e9 de Picardie Jules Verne, 80080 Amiens, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1145\/636772.636795","article-title":"What\u2019s in the eyes for attentive input","volume":"46","author":"Zhai","year":"2003","journal-title":"Commun. 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