@article{GotzHahn2022Criti-59105,
  year={2022},
  doi={10.1371/journal.pone.0269715},
  title={Critical analysis on the reproducibility of visual quality assessment using deep features},
  number={8},
  volume={17},
  journal={PLoS ONE},
  author={Götz-Hahn, Franz and Hosu, Vlad and Saupe, Dietmar},
  note={Article Number: e0269715}
}

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    <dc:rights>Attribution 4.0 International</dc:rights>
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    <dcterms:abstract xml:lang="eng">Data used to train supervised machine learning models are commonly split into independent training, validation, and test sets. This paper illustrates that complex data leakage cases have occurred in the no-reference image and video quality assessment literature. Recently, papers in several journals reported performance results well above the best in the field. However, our analysis shows that information from the test set was inappropriately used in the training process in different ways and that the claimed performance results cannot be achieved. When correcting for the data leakage, the performances of the approaches drop even below the state-of-the-art by a large margin. Additionally, we investigate end-to-end variations to the discussed approaches, which do not improve upon the original.</dcterms:abstract>
    <dc:creator>Hosu, Vlad</dc:creator>
    <dc:creator>Saupe, Dietmar</dc:creator>
    <dcterms:title>Critical analysis on the reproducibility of visual quality assessment using deep features</dcterms:title>
    <dc:creator>Götz-Hahn, Franz</dc:creator>
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    <dc:contributor>Hosu, Vlad</dc:contributor>
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    <dc:contributor>Götz-Hahn, Franz</dc:contributor>
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    <dcterms:issued>2022</dcterms:issued>
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