February 7, 2020 Dataset Open Access

Anonymous

DataCite XML Export

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  <identifier identifierType="DOI">10.5281/zenodo.3672132</identifier>
  <creators>
    <creator>
      <creatorName>Anonymous</creatorName>
      <affiliation>Anonymous</affiliation>
    </creator>
  </creators>
  <titles>
    <title>SlimageNet64</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Few-Shot Learning, Continual Few-Shot Learning, Meta-Learning, Benchmark, Benchmarks, Dataset, Datasets</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-02-07</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3672132</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3672131</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;SlimageNet64 is new variant of ImageNet64&amp;times;64&amp;nbsp;(Chrabaszcz et al., 2017),&amp;nbsp;derived&lt;br&gt;
from Slim and ImageNet. SlimageNet64 is ideal for few-shot learning, continual learning and meta-learning research. It consists of 200 instances from each of the 1000 object categories of the ILSVRC-2012 dataset (Krizhevsky et al., 2012; Russakovsky et al., 2015), for a total of 200K RGB images with a resolution of 64 &amp;times; 64 &amp;times; 3 pixels. We created this dataset from the downscaled version of ILSVRC-2012, ImageNet64x64, as reported in (Chrabaszcz et al., 2017), using the box downsampling Pillow library.&lt;/p&gt;</description>
  </descriptions>
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