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Reported on 12 benchmarks across 2 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision6 results

  • Image ClassificationonCIFAR-10N-Random2
    Accuracy (mean)· 2019-01-14
    89.47
    best: 96.21 (PSSCL)
    How does Disagreement Help Generalization against Label Corruption?arXiv:1901.04215
  • Image ClassificationonCIFAR-10N-Random3
    Accuracy (mean)· 2019-01-14
    89.54
    best: 96.49 (PSSCL)
    How does Disagreement Help Generalization against Label Corruption?arXiv:1901.04215
  • Image ClassificationonCIFAR-10N-Aggregate
    Accuracy (mean)· 2019-01-14
    90.61
    best: 97.39 (ProMix)
    How does Disagreement Help Generalization against Label Corruption?arXiv:1901.04215
  • Image ClassificationonCIFAR-10N-Random1
    Accuracy (mean)· 2019-01-14
    89.7
    best: 96.97 (ProMix)
    How does Disagreement Help Generalization against Label Corruption?arXiv:1901.04215
  • Image ClassificationonCIFAR-100N
    Accuracy (mean)· 2019-01-14
    57.88
    best: 74.08 (PGDF)
    How does Disagreement Help Generalization against Label Corruption?arXiv:1901.04215
  • Image ClassificationonCIFAR-10N-Worst
    Accuracy (mean)· 2019-01-14
    83.26
    best: 96.16 (ProMix)
    How does Disagreement Help Generalization against Label Corruption?arXiv:1901.04215

Medical6 results

  • Document Text ClassificationonCIFAR-10N-Random2
    Accuracy (mean)· 2019-01-14
    89.47
    best: 96.21 (PSSCL)
    How does Disagreement Help Generalization against Label Corruption?arXiv:1901.04215
  • Document Text ClassificationonCIFAR-10N-Random3
    Accuracy (mean)· 2019-01-14
    89.54
    best: 96.49 (PSSCL)
    How does Disagreement Help Generalization against Label Corruption?arXiv:1901.04215
  • Document Text ClassificationonCIFAR-10N-Aggregate
    Accuracy (mean)· 2019-01-14
    90.61
    best: 97.39 (ProMix)
    How does Disagreement Help Generalization against Label Corruption?arXiv:1901.04215
  • Document Text ClassificationonCIFAR-10N-Random1
    Accuracy (mean)· 2019-01-14
    89.7
    best: 96.97 (ProMix)
    How does Disagreement Help Generalization against Label Corruption?arXiv:1901.04215
  • Document Text ClassificationonCIFAR-100N
    Accuracy (mean)· 2019-01-14
    57.88
    best: 74.08 (PGDF)
    How does Disagreement Help Generalization against Label Corruption?arXiv:1901.04215
  • Document Text ClassificationonCIFAR-10N-Worst
    Accuracy (mean)· 2019-01-14
    83.26
    best: 96.16 (ProMix)
    How does Disagreement Help Generalization against Label Corruption?arXiv:1901.04215