@prefix this: <https://w3id.org/sciencelive/np/RAW3U5bhevQdUMc51N_QsJSuCrV8j8QauGh9cU6H0o-2Q> .
@prefix sub: <https://w3id.org/sciencelive/np/RAW3U5bhevQdUMc51N_QsJSuCrV8j8QauGh9cU6H0o-2Q/> .
@prefix np: <http://www.nanopub.org/nschema#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix prov: <http://www.w3.org/ns/prov#> .
@prefix npx: <http://purl.org/nanopub/x/> .
@prefix dc: <http://purl.org/dc/terms/> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

sub:Head {
  this: a np:Nanopublication;
    np:hasAssertion sub:assertion;
    np:hasProvenance sub:provenance;
    np:hasPublicationInfo sub:pubinfo .
}

sub:assertion {
  sub:supervised-pretraining a <https://w3id.org/sciencelive/o/terms/FORRT-Replication-Study>,
      <https://w3id.org/sciencelive/o/terms/Replication-Study>;
    <http://www.w3.org/2000/01/rdf-schema#label> "Supervised pretraining as alternative to episodic meta-learning for satellite imagery classification";
    <http://www.w3.org/2004/02/skos/core#related> <http://www.wikidata.org/entity/Q110797734>,
      <http://www.wikidata.org/entity/Q197536>, <http://www.wikidata.org/entity/Q334384>,
      <http://www.wikidata.org/entity/Q6027324>;
    <https://w3id.org/sciencelive/o/terms/hasDeviationDescription> "Guo et al. trained Prototypical Networks episodically (40,000 episodes, approximately 3 hours). We replaced episodic training with standard supervised classification (10 epochs, approximately 15 minutes). Same backbone and image resolution. This tests whether the training method matters for cross-domain transfer, or whether the backbone features alone are sufficient.";
    <https://w3id.org/sciencelive/o/terms/hasDiscipline> <http://www.wikidata.org/entity/Q21198>;
    <https://w3id.org/sciencelive/o/terms/hasMethodologyDescription> "We trained a ResNet-10 backbone (4.9 million parameters, 224×224 pixel images) using standard supervised classification on mini-ImageNet's 64 object categories for 10 epochs with data augmentation. At test time, we froze the backbone and used Prototypical Network-style nearest-prototype classification on EuroSAT (27,000 real Sentinel-2 satellite patches). This approach requires no meta-learning framework — only standard PyTorch classification training. Evaluation over 100 random 5-way tasks with 5, 20, and 50 labeled examples.";
    <https://w3id.org/sciencelive/o/terms/hasScopeDescription> "Testing whether standard supervised pretraining — training a model to classify everyday objects using conventional classification — achieves comparable cross-domain few-shot accuracy on satellite imagery to the episodic meta-learning approach used by Guo et al. (2020). ";
    <https://w3id.org/sciencelive/o/terms/targetsClaim> <https://w3id.org/np/RAC32x9lWdLTKRpiz7utsMmLf0_JzadtA4kCvvbrJ_KK8/supervised-pretraining> .
}

sub:provenance {
  sub:assertion prov:wasAttributedTo <https://orcid.org/0000-0002-1784-2920> .
}

sub:pubinfo {
  <https://orcid.org/0000-0002-1784-2920> <http://xmlns.com/foaf/0.1/name> "Anne Fouilloux" .
  
  this: dc:created "2026-04-18T16:01:09.256Z"^^xsd:dateTime;
    dc:creator <https://orcid.org/0000-0002-1784-2920>;
    dc:license <https://creativecommons.org/licenses/by/4.0/>;
    npx:introduces sub:supervised-pretraining;
    npx:wasCreatedAt <https://platform.sciencelive4all.org>;
    <http://www.w3.org/2000/01/rdf-schema#label> "NP created using Declaring a replication study design according to FORRT";
    <https://w3id.org/np/o/ntemplate/wasCreatedFromTemplate> <https://w3id.org/np/RAuLEjPp-4dTvPwMkfHggTto1CgjIftiGRAgHlyeEonjQ> .
  
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    npx:signedBy <https://orcid.org/0000-0002-1784-2920> .
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