Conference paper Open Access

Towards Visual Anomaly Detection in Domains with Limited Amount of Labeled Data

Štepec, Dejan; Skočaj, Danijel


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="URL">https://zenodo.org/record/3935533</identifier>
  <creators>
    <creator>
      <creatorName>Štepec, Dejan</creatorName>
      <givenName>Dejan</givenName>
      <familyName>Štepec</familyName>
      <affiliation>XLAB Research</affiliation>
    </creator>
    <creator>
      <creatorName>Skočaj, Danijel</creatorName>
      <givenName>Danijel</givenName>
      <familyName>Skočaj</familyName>
      <affiliation>University of Ljubljana</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Towards Visual Anomaly Detection in Domains with Limited Amount of Labeled Data</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>anomaly detection</subject>
    <subject>unsupervised</subject>
    <subject>deep-learning</subject>
    <subject>autoencoders</subject>
    <subject>generative adversarial networks</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-03-19</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3935533</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.18690/978-961-286-337-1</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/ipc</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Anomaly detection in visual data refers to the problem of differentiating abnormal appearances from normal cases. Supervised approaches have been successfully applied to different domains, but require abundance of labeled data. Due to the nature of how anomalies occur and their underlying generating processes, it is hard to characterize and label them. Recent advances in deep generative based models have sparked interest towards applying such methods for unsupervised anomaly detection and have shown promising results in medical and industrial inspection domains.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/826121/">826121</awardNumber>
      <awardTitle>individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
15
13
views
downloads
Views 15
Downloads 13
Data volume 175.2 MB
Unique views 13
Unique downloads 12

Share

Cite as