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Models/F-div

F-div

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)· 2020-11-07
89.79
best: 96.21 (PSSCL)
When Optimizing $f$-divergence is Robust with Label Noise arXiv:2011.03687
Image ClassificationonCIFAR-10N-Random3
Accuracy (mean)· 2020-11-07
89.55
best: 96.49 (PSSCL)
When Optimizing $f$-divergence is Robust with Label Noise arXiv:2011.03687
Image ClassificationonCIFAR-10N-Aggregate
Accuracy (mean)· 2020-11-07
91.64
best: 97.39 (ProMix)
When Optimizing $f$-divergence is Robust with Label Noise arXiv:2011.03687
Image ClassificationonCIFAR-10N-Random1
Accuracy (mean)· 2020-11-07
89.7
best: 96.97 (ProMix)
When Optimizing $f$-divergence is Robust with Label Noise arXiv:2011.03687
Image ClassificationonCIFAR-100N
Accuracy (mean)· 2020-11-07
57.1
best: 74.08 (PGDF)
When Optimizing $f$-divergence is Robust with Label Noise arXiv:2011.03687
Image ClassificationonCIFAR-10N-Worst
Accuracy (mean)· 2020-11-07
82.53
best: 96.16 (ProMix)
When Optimizing $f$-divergence is Robust with Label Noise arXiv:2011.03687

Medical6 results

Document Text ClassificationonCIFAR-10N-Random2
Accuracy (mean)· 2020-11-07
89.79
best: 96.21 (PSSCL)
When Optimizing $f$-divergence is Robust with Label Noise arXiv:2011.03687
Document Text ClassificationonCIFAR-10N-Random3
Accuracy (mean)· 2020-11-07
89.55
best: 96.49 (PSSCL)
When Optimizing $f$-divergence is Robust with Label Noise arXiv:2011.03687
Document Text ClassificationonCIFAR-10N-Aggregate
Accuracy (mean)· 2020-11-07
91.64
best: 97.39 (ProMix)
When Optimizing $f$-divergence is Robust with Label Noise arXiv:2011.03687
Document Text ClassificationonCIFAR-10N-Random1
Accuracy (mean)· 2020-11-07
89.7
best: 96.97 (ProMix)
When Optimizing $f$-divergence is Robust with Label Noise arXiv:2011.03687
Document Text ClassificationonCIFAR-100N
Accuracy (mean)· 2020-11-07
57.1
best: 74.08 (PGDF)
When Optimizing $f$-divergence is Robust with Label Noise arXiv:2011.03687
Document Text ClassificationonCIFAR-10N-Worst
Accuracy (mean)· 2020-11-07
82.53
best: 96.16 (ProMix)
When Optimizing $f$-divergence is Robust with Label Noise arXiv:2011.03687