{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T13:03:15Z","timestamp":1776862995869,"version":"3.51.2"},"reference-count":50,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T00:00:00Z","timestamp":1705363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Sciences and Engineering Research Council of Canada"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Few-shot learning aims to identify unseen classes with limited labelled data. Recent few-shot learning techniques have shown success in generalizing to unseen classes; however, the performance of these techniques has also been shown to degrade when tested on an out-of-domain setting. Previous work, additionally, has also demonstrated increasing reliance on supervised finetuning in an off-line or online capacity. This paper proposes a novel, fully self-supervised few-shot learning technique (FSS) that utilizes a vision transformer and masked autoencoder. The proposed technique can generalize to out-of-domain classes by finetuning the model in a fully self-supervised method for each episode. We evaluate the proposed technique using three datasets (all out-of-domain). As such, our results show that FSS has an accuracy gain of 1.05%, 0.12%, and 1.28% on the ISIC, EuroSat, and BCCD datasets, respectively, without the use of supervised training.<\/jats:p>","DOI":"10.3390\/jimaging10010023","type":"journal-article","created":{"date-parts":[[2024,1,16]],"date-time":"2024-01-16T11:37:18Z","timestamp":1705405038000},"page":"23","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Fully Self-Supervised Out-of-Domain Few-Shot Learning with Masked Autoencoders"],"prefix":"10.3390","volume":"10","author":[{"given":"Reece","family":"Walsh","sequence":"first","affiliation":[{"name":"Irving K. Barber Faculty of Science, University of British Columbia, Kelowna, BC V1V 1V7, Canada"}]},{"given":"Islam","family":"Osman","sequence":"additional","affiliation":[{"name":"Irving K. Barber Faculty of Science, University of British Columbia, Kelowna, BC V1V 1V7, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2416-5700","authenticated-orcid":false,"given":"Omar","family":"Abdelaziz","sequence":"additional","affiliation":[{"name":"Irving K. Barber Faculty of Science, University of British Columbia, Kelowna, BC V1V 1V7, Canada"}]},{"given":"Mohamed S.","family":"Shehata","sequence":"additional","affiliation":[{"name":"Irving K. Barber Faculty of Science, University of British Columbia, Kelowna, BC V1V 1V7, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bateni, P., Goyal, R., Masrani, V., Wood, F., and Sigal, L. (2020, January 14\u201319). Improved few-shot visual classification. 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