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Models/CORES

CORES

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)· 2020-10-05
89.91
best: 96.21 (PSSCL)
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach arXiv:2010.02347
Image ClassificationonCIFAR-10N-Random3
Accuracy (mean)· 2020-10-05
89.79
best: 96.49 (PSSCL)
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach arXiv:2010.02347
Image ClassificationonCIFAR-10N-Aggregate
Accuracy (mean)· 2020-10-05
91.23
best: 97.39 (ProMix)
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach arXiv:2010.02347
Image ClassificationonCIFAR-10N-Random1
Accuracy (mean)· 2020-10-05
89.66
best: 96.97 (ProMix)
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach arXiv:2010.02347
Image ClassificationonCIFAR-100N
Accuracy (mean)· 2020-10-05
61.15
best: 74.08 (PGDF)
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach arXiv:2010.02347
Image ClassificationonCIFAR-10N-Worst
Accuracy (mean)· 2020-10-05
83.6
best: 96.16 (ProMix)
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach arXiv:2010.02347

Medical6 results

Document Text ClassificationonCIFAR-10N-Random2
Accuracy (mean)· 2020-10-05
89.91
best: 96.21 (PSSCL)
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach arXiv:2010.02347
Document Text ClassificationonCIFAR-10N-Random3
Accuracy (mean)· 2020-10-05
89.79
best: 96.49 (PSSCL)
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach arXiv:2010.02347
Document Text ClassificationonCIFAR-10N-Aggregate
Accuracy (mean)· 2020-10-05
91.23
best: 97.39 (ProMix)
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach arXiv:2010.02347
Document Text ClassificationonCIFAR-10N-Random1
Accuracy (mean)· 2020-10-05
89.66
best: 96.97 (ProMix)
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach arXiv:2010.02347
Document Text ClassificationonCIFAR-100N
Accuracy (mean)· 2020-10-05
61.15
best: 74.08 (PGDF)
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach arXiv:2010.02347
Document Text ClassificationonCIFAR-10N-Worst
Accuracy (mean)· 2020-10-05
83.6
best: 96.16 (ProMix)
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach arXiv:2010.02347