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ResearchFormerly WizWand

Image Classification on BloodMNIST (test)

96.71AccuracyDistributional Loss
41.735656.007870.2884.5522Jun 11, 2026Jun 15, 2026Jun 20, 2026Jun 24, 2026Jun 29, 2026Jul 3, 2026Jul 8, 2026
Updated 3mo ago

Evaluation results

MethodLinks
Distributional LossSamples per Class=full dataset, Density Estimate=KDE2026.0696.71
T-vMFSamples per Class=full dataset2026.0696.05
CESamples per Class=full dataset2026.0695.4
PolyLossSamples per Class=full dataset2026.0695.29
Distributional LossSamples per Class=200, Density Estimate=KDE2026.0692.32
PolyLossSamples per Class=2002026.0692.14
T-vMFSamples per Class=2002026.0692.01
CESamples per Class=2002026.0690.06
Distributional LossSamples per Class=20, Density Estimate=KDE2026.0683.14
T-vMFSamples per Class=202026.0679.9
PolyLossSamples per Class=202026.0678.65
FedKT-CSD (DP)Data Partitioning=Path.2026.0775.19
FedKT-CSD (DP)Data Partitioning=α=0.12026.0775.03
FedKT-CSD (DP)Data Partitioning=α=0.052026.0774.84
CESamples per Class=202026.0673.35
FedSD2CData Partitioning=α=0.12026.0769.52
FedSD2CData Partitioning=Path.2026.0765.47
FedSD2CData Partitioning=α=0.052026.0761.83
Distributional LossSamples per Class=2, Density Estimate=KDE2026.0661.82
DENSEData Partitioning=α=0.12026.0758.46
T-vMFSamples per Class=22026.0655.45
DENSEData Partitioning=Path.2026.0754.83
CoBoostingData Partitioning=α=0.12026.0754.82
FedCVAEData Partitioning=Path.2026.0751.83
PolyLossSamples per Class=22026.0651.63
CoBoostingData Partitioning=Path.2026.0751.47
FedD3Data Partitioning=Path.2026.0750.27
FedCVAEData Partitioning=α=0.12026.0750.15
DENSEData Partitioning=α=0.052026.0748.72
FedD3Data Partitioning=α=0.12026.0748.36
CoBoostingData Partitioning=α=0.052026.0745.63
FedCVAEData Partitioning=α=0.052026.0745.28
CESamples per Class=22026.0644.63
FedD3Data Partitioning=α=0.052026.0743.85