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  dc:created "2026-04-18T14:58:01.411Z"^^xsd:dateTime;
  dc:creator <https://orcid.org/0000-0002-1784-2920>;
  dc:license <https://creativecommons.org/licenses/by/4.0/>;
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  <http://www.w3.org/2000/01/rdf-schema#label> "Paper annotation: The results demonstrate that state-of-art meta-learning methods are surprisingly outperformed by earlier meta-learning approaches, and all meta-learning methods underperform in relation to simple fine-tuning by 12.8% average accuracy. ";
  <https://w3id.org/np/o/ntemplate/wasCreatedFromTemplate> <https://w3id.org/np/RA24onqmqTMsraJ7ypYFOuckmNWpo4Zv5gsLqhXt7xYPU> .

<https://doi.org/10.1007/978-3-030-58583-9_8> <http://purl.org/spar/cito/hasQuotedText>
    "The results demonstrate that state-of-art meta-learning methods are surprisingly outperformed by earlier meta-learning approaches, and all meta-learning methods underperform in relation to simple fine-tuning by 12.8% average accuracy. ";
  <http://www.w3.org/2000/01/rdf-schema#comment> "This finding has important implications for Earth observation. If simple fine tuning outperforms complex meta learning for cross domain few shot classification, environmental scientists working with limited labeled satellite data can rely on standard supervised learning. This applies to tasks like habitat monitoring, land cover change, and biodiversity assessment, lowering the barrier to using AI in environmental research." .

<https://w3id.org/np/o/ntemplate/CREATOR> <http://purl.org/spar/cito/quotes> <https://doi.org/10.1007/978-3-030-58583-9_8> .

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<https://orcid.org/0000-0002-1784-2920> <http://xmlns.com/foaf/0.1/name> "Anne Fouilloux" .

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