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Models/Divide-Mix

Divide-Mix

Reported on 12 benchmarks across 2 tasks · 1 paper · 8 SOTA

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)· 2020-02-18
90.9
best: 96.21 (PSSCL)
SOTA
DivideMix: Learning with Noisy Labels as Semi-supervised Learning arXiv:2002.07394
Image ClassificationonCIFAR-10N-Aggregate
Accuracy (mean)· 2020-02-18
95.01
best: 97.39 (ProMix)
SOTA
DivideMix: Learning with Noisy Labels as Semi-supervised Learning arXiv:2002.07394
Image ClassificationonCIFAR-100N
Accuracy (mean)· 2020-02-18
71.13
best: 74.08 (PGDF)
SOTA
DivideMix: Learning with Noisy Labels as Semi-supervised Learning arXiv:2002.07394
Image ClassificationonCIFAR-10N-Worst
Accuracy (mean)· 2020-02-18
92.56
best: 96.16 (ProMix)
SOTA
DivideMix: Learning with Noisy Labels as Semi-supervised Learning arXiv:2002.07394
Image ClassificationonCIFAR-10N-Random3
Accuracy (mean)· 2020-02-18
89.97
best: 96.49 (PSSCL)
DivideMix: Learning with Noisy Labels as Semi-supervised Learning arXiv:2002.07394
Image ClassificationonCIFAR-10N-Random1
Accuracy (mean)· 2020-02-18
90.18
best: 96.97 (ProMix)
DivideMix: Learning with Noisy Labels as Semi-supervised Learning arXiv:2002.07394

Medical6 results

Document Text ClassificationonCIFAR-10N-Random2
Accuracy (mean)· 2020-02-18
90.9
best: 96.21 (PSSCL)
SOTA
DivideMix: Learning with Noisy Labels as Semi-supervised Learning arXiv:2002.07394
Document Text ClassificationonCIFAR-10N-Aggregate
Accuracy (mean)· 2020-02-18
95.01
best: 97.39 (ProMix)
SOTA
DivideMix: Learning with Noisy Labels as Semi-supervised Learning arXiv:2002.07394
Document Text ClassificationonCIFAR-100N
Accuracy (mean)· 2020-02-18
71.13
best: 74.08 (PGDF)
SOTA
DivideMix: Learning with Noisy Labels as Semi-supervised Learning arXiv:2002.07394
Document Text ClassificationonCIFAR-10N-Worst
Accuracy (mean)· 2020-02-18
92.56
best: 96.16 (ProMix)
SOTA
DivideMix: Learning with Noisy Labels as Semi-supervised Learning arXiv:2002.07394
Document Text ClassificationonCIFAR-10N-Random3
Accuracy (mean)· 2020-02-18
89.97
best: 96.49 (PSSCL)
DivideMix: Learning with Noisy Labels as Semi-supervised Learning arXiv:2002.07394
Document Text ClassificationonCIFAR-10N-Random1
Accuracy (mean)· 2020-02-18
90.18
best: 96.97 (ProMix)
DivideMix: Learning with Noisy Labels as Semi-supervised Learning arXiv:2002.07394