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Models/SOP+

SOP+

Reported on 10 benchmarks across 2 tasks · 1 paper · 2 SOTA

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

Computer Vision5 results

Image ClassificationonCIFAR-10N-Random3
Accuracy (mean)· 2022-02-28
95.39
best: 96.49 (PSSCL)
SOTA
Robust Training under Label Noise by Over-parameterization arXiv:2202.14026
Image ClassificationonCIFAR-10N-Aggregate
Accuracy (mean)· 2022-02-28
95.61
best: 97.39 (ProMix)
Robust Training under Label Noise by Over-parameterization arXiv:2202.14026
Image ClassificationonCIFAR-10N-Random1
Accuracy (mean)· 2022-02-28
95.28
best: 96.97 (ProMix)
Robust Training under Label Noise by Over-parameterization arXiv:2202.14026
Image ClassificationonCIFAR-100N
Accuracy (mean)· 2022-02-28
67.81
best: 74.08 (PGDF)
Robust Training under Label Noise by Over-parameterization arXiv:2202.14026
Image ClassificationonCIFAR-10N-Worst
Accuracy (mean)· 2022-02-28
93.24
best: 96.16 (ProMix)
Robust Training under Label Noise by Over-parameterization arXiv:2202.14026

Medical5 results

Document Text ClassificationonCIFAR-10N-Random3
Accuracy (mean)· 2022-02-28
95.39
best: 96.49 (PSSCL)
SOTA
Robust Training under Label Noise by Over-parameterization arXiv:2202.14026
Document Text ClassificationonCIFAR-10N-Aggregate
Accuracy (mean)· 2022-02-28
95.61
best: 97.39 (ProMix)
Robust Training under Label Noise by Over-parameterization arXiv:2202.14026
Document Text ClassificationonCIFAR-10N-Random1
Accuracy (mean)· 2022-02-28
95.28
best: 96.97 (ProMix)
Robust Training under Label Noise by Over-parameterization arXiv:2202.14026
Document Text ClassificationonCIFAR-100N
Accuracy (mean)· 2022-02-28
67.81
best: 74.08 (PGDF)
Robust Training under Label Noise by Over-parameterization arXiv:2202.14026
Document Text ClassificationonCIFAR-10N-Worst
Accuracy (mean)· 2022-02-28
93.24
best: 96.16 (ProMix)
Robust Training under Label Noise by Over-parameterization arXiv:2202.14026