Publikation:

Decontextualized learning for interpretable hierarchical representations of visual patterns

Lade...
Vorschaubild

Dateien

Etheredge_2-1gmjjj99lu4t78.pdf
Etheredge_2-1gmjjj99lu4t78.pdfGröße: 4.07 MBDownloads: 190

Datum

2021

Autor:innen

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

ArXiv-ID

Internationale Patentnummer

Link zur Lizenz

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Gold
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published

Erschienen in

Patterns. Cell Press. 2021, 2(2), 100193. eISSN 2666-3899. Available under: doi: 10.1016/j.patter.2020.100193

Zusammenfassung

Apart from discriminative modeling, the application of deep convolutional neural networks to basic research utilizing natural imaging data faces unique hurdles. Here, we present decontextualized hierarchical representation learning (DHRL), designed specifically to overcome these limitations. DHRL enables the broader use of small datasets, which are typical in most studies. It also captures spatial relationships between features, provides novel tools for investigating latent variables, and achieves state-of-the-art disentanglement scores on small datasets. DHRL is enabled by a novel preprocessing technique inspired by generative model chaining and an improved ladder network architecture and regularization scheme. More than an analytical tool, DHRL enables novel capabilities for virtual experiments performed directly on a latent representation, which may transform the way we perform investigations of natural image features, directly integrating analytical, empirical, and theoretical approaches.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
570 Biowissenschaften, Biologie

Schlagwörter

Konferenz

Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690ETHEREDGE, Robert Ian, Manfred SCHARTL, Alex JORDAN, 2021. Decontextualized learning for interpretable hierarchical representations of visual patterns. In: Patterns. Cell Press. 2021, 2(2), 100193. eISSN 2666-3899. Available under: doi: 10.1016/j.patter.2020.100193
BibTex
@article{Etheredge2021-02Decon-52942,
  year={2021},
  doi={10.1016/j.patter.2020.100193},
  title={Decontextualized learning for interpretable hierarchical representations of visual patterns},
  number={2},
  volume={2},
  journal={Patterns},
  author={Etheredge, Robert Ian and Schartl, Manfred and Jordan, Alex},
  note={Article Number: 100193}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/52942">
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <dcterms:issued>2021-02</dcterms:issued>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by-nc-nd/4.0/"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/>
    <dc:contributor>Jordan, Alex</dc:contributor>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/52942/1/Etheredge_2-1gmjjj99lu4t78.pdf"/>
    <dc:creator>Schartl, Manfred</dc:creator>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-02-19T12:58:16Z</dcterms:available>
    <dc:creator>Jordan, Alex</dc:creator>
    <dc:rights>Attribution-NonCommercial-NoDerivatives 4.0 International</dc:rights>
    <dc:language>eng</dc:language>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:abstract xml:lang="eng">Apart from discriminative modeling, the application of deep convolutional neural networks to basic research utilizing natural imaging data faces unique hurdles. Here, we present decontextualized hierarchical representation learning (DHRL), designed specifically to overcome these limitations. DHRL enables the broader use of small datasets, which are typical in most studies. It also captures spatial relationships between features, provides novel tools for investigating latent variables, and achieves state-of-the-art disentanglement scores on small datasets. DHRL is enabled by a novel preprocessing technique inspired by generative model chaining and an improved ladder network architecture and regularization scheme. More than an analytical tool, DHRL enables novel capabilities for virtual experiments performed directly on a latent representation, which may transform the way we perform investigations of natural image features, directly integrating analytical, empirical, and theoretical approaches.</dcterms:abstract>
    <dcterms:title>Decontextualized learning for interpretable hierarchical representations of visual patterns</dcterms:title>
    <dc:creator>Etheredge, Robert Ian</dc:creator>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/52942"/>
    <dc:contributor>Schartl, Manfred</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/52942/1/Etheredge_2-1gmjjj99lu4t78.pdf"/>
    <dc:contributor>Etheredge, Robert Ian</dc:contributor>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-02-19T12:58:16Z</dc:date>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Unbekannt
Diese Publikation teilen