{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wacquet G"],"funding":["French State, the French Region Hauts-de-France and Ifremer","European Union","CPER MARCO","S3-EUROHAB","European Commission's H2020 Framework Programme","European Commission&apos;s H2020 Framework Programme","EUtROphication and Harmful Algal Bloom"],"pagination":["5469-5471"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9750126"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["38(24)"],"pubmed_abstract":["<h4>Summary</h4>In recent years, Deep Learning (DL) has been increasingly used in many fields, in particular in image recognition, due to its ability to solve problems where traditional machine learning algorithms fail. However, building an appropriate DL model from scratch, especially in the context of ecological studies, is a difficult task due to the dynamic nature and morphological variability of living organisms, as well as the high cost in terms of time, human resources and skills required to label a large number of training images. To overcome this problem, Transfer Learning (TL) can be used to improve a classifier by transferring information learnt from many domains thanks to a very large training set composed of various images, to another domain with a smaller amount of training d"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["EcoTransLearn: an R-package to easily use transfer learning for ecological studies-a plankton case study."],"pmcid":["PMC9750126"],"funding_grant_id":["871153"],"pubmed_authors":["Wacquet G","Lefebvre A"],"additional_accession":[]},"is_claimable":false,"name":"EcoTransLearn: an R-package to easily use transfer learning for ecological studies-a plankton case study.","description":"<h4>Summary</h4>In recent years, Deep Learning (DL) has been increasingly used in many fields, in particular in image recognition, due to its ability to solve problems where traditional machine learning algorithms fail. However, building an appropriate DL model from scratch, especially in the context of ecological studies, is a difficult task due to the dynamic nature and morphological variability of living organisms, as well as the high cost in terms of time, human resources and skills required to label a large number of training images. To overcome this problem, Transfer Learning (TL) can be used to improve a classifier by transferring information learnt from many domains thanks to a very large training set composed of various images, to another domain with a smaller amount of training d","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2026-05-28T06:40:55.802Z","creation":"2024-11-07T00:43:31.293Z"},"accession":"S-EPMC9750126","cross_references":{"pubmed":["36282847"],"doi":["10.1093/bioinformatics/btac703"]}}